{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System-Level Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to perform system-level simulations with Sionna \n",
    "by integrating key functionalities from the SYS and PHY modules, including: \n",
    "- Base station placement on a a hexagonal spiral grid\n",
    "- User drop within each sector\n",
    "- 3GPP-compliant channel generation\n",
    "- Proportional fair scheduler\n",
    "- Power control\n",
    "- Link adaptation for adaptive modulation and coding scheme (MCS) selection\n",
    "- Post-equalization signal-to-interference-plus-noise (SINR) computation\n",
    "- Physical layer abstraction\n",
    "\n",
    "Below is the diagram flow underlying the system-level simulator we will\n",
    "build in this notebook.\n",
    "\n",
    "This is an advanced notebook. We recommend first exploring the tutorials on \n",
    "[physical layer abstraction](https://nvlabs.github.io/sionna/sys/tutorials/PHY_Abstraction.html), [link\n",
    "adaptation](https://nvlabs.github.io/sionna/sys/tutorials/LinkAdaptation.html), and [scheduling](https://nvlabs.github.io/sionna/sys/tutorials/Scheduling.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![e2e_notebook.png]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "We start by importing Sionna and the relevant external libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:19.579285Z",
     "iopub.status.busy": "2025-03-14T11:09:19.578755Z",
     "iopub.status.idle": "2025-03-14T11:09:22.557093Z",
     "shell.execute_reply": "2025-03-14T11:09:22.555674Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n",
    "    gpu_num = 0 # Use \"\" to use the CPU\n",
    "    if gpu_num!=\"\":\n",
    "        print(f'\\nUsing GPU {gpu_num}\\n')\n",
    "    else:\n",
    "        print('\\nUsing CPU\\n')\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.sys\n",
    "except ImportError as e:\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
    "import tensorflow as tf\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        tf.config.experimental.set_memory_growth(gpus[0], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.562460Z",
     "iopub.status.busy": "2025-03-14T11:09:22.561818Z",
     "iopub.status.idle": "2025-03-14T11:09:22.778039Z",
     "shell.execute_reply": "2025-03-14T11:09:22.777176Z"
    }
   },
   "outputs": [],
   "source": [
    "# Additional external libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Sionna components\n",
    "from sionna.sys.utils import spread_across_subcarriers\n",
    "from sionna.sys import PHYAbstraction, \\\n",
    "    OuterLoopLinkAdaptation, gen_hexgrid_topology, \\\n",
    "    get_pathloss, open_loop_uplink_power_control, downlink_fair_power_control, \\\n",
    "    get_num_hex_in_grid, PFSchedulerSUMIMO\n",
    "from sionna.phy.constants import BOLTZMANN_CONSTANT\n",
    "from sionna.phy.utils import db_to_lin, dbm_to_watt, log2, insert_dims\n",
    "from sionna.phy import config, dtypes, Block\n",
    "from sionna.phy.channel.tr38901 import UMi, UMa, RMa, PanelArray\n",
    "from sionna.phy.channel import GenerateOFDMChannel\n",
    "from sionna.phy.mimo import StreamManagement\n",
    "from sionna.phy.ofdm import ResourceGrid, RZFPrecodedChannel, EyePrecodedChannel, \\\n",
    "    LMMSEPostEqualizationSINR\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "sionna.phy.config.seed = 42\n",
    "\n",
    "# Internal computational precision\n",
    "sionna.phy.config.precision = 'single'  # 'single' or 'double'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utils\n",
    "\n",
    "We will now define several auxiliary functions that are used in this notebook, including:\n",
    "- Channel matrix evolution;\n",
    "- User stream management;\n",
    "- Signal-to-interference-plus-noise-ratio (SINR) computation;\n",
    "- Achievable rate estimation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Channel matrix generation\n",
    "\n",
    "We assume that channel matrix coefficients are regenerated from a specified 3GPP\n",
    "model every `coherence_time` slots, following a typical block fading model.\n",
    "For simplicity, we emulate the impact of fast fading by multiplying the channel\n",
    "matrix by a factor that follows an autoregressive process evolving at each slot.\n",
    "\n",
    "More realistic simulations would include channel evolution based on user\n",
    "mobility in a ray-traced enviroment, as for example shown in\n",
    "the notebook [Sionna SYS meets Sionna RT](https://nvlabs.github.com/sionna/sys/tutorials/SYS_Meets_RT.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.782481Z",
     "iopub.status.busy": "2025-03-14T11:09:22.782113Z",
     "iopub.status.idle": "2025-03-14T11:09:22.791460Z",
     "shell.execute_reply": "2025-03-14T11:09:22.790740Z"
    }
   },
   "outputs": [],
   "source": [
    "class ChannelMatrix(Block):\n",
    "    def __init__(self,\n",
    "                 resource_grid,\n",
    "                 batch_size,\n",
    "                 num_rx,\n",
    "                 num_tx,\n",
    "                 coherence_time,\n",
    "                 precision=None):\n",
    "        super().__init__(precision=precision)\n",
    "        self.resource_grid = resource_grid\n",
    "        self.coherence_time = coherence_time\n",
    "        self.batch_size = batch_size\n",
    "        # Fading autoregressive coefficient initialization\n",
    "        self.rho_fading = config.tf_rng.uniform([batch_size, num_rx, num_tx],\n",
    "                                                minval=.95,\n",
    "                                                maxval=.99,\n",
    "                                                dtype=self.rdtype)\n",
    "        # Fading initialization\n",
    "        self.fading = tf.ones([batch_size, num_rx, num_tx],\n",
    "                              dtype=self.rdtype)\n",
    "\n",
    "    def call(self, channel_model):\n",
    "        \"\"\" Generate OFDM channel matrix\"\"\"\n",
    "\n",
    "        # Instantiate the OFDM channel generator\n",
    "        ofdm_channel = GenerateOFDMChannel(channel_model,\n",
    "                                           self.resource_grid)\n",
    "\n",
    "        # [batch_size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_ofdm_symbols, num_subcarriers]\n",
    "        h_freq = ofdm_channel(self.batch_size)\n",
    "        return h_freq\n",
    "\n",
    "    def update(self,\n",
    "               channel_model,\n",
    "               h_freq,\n",
    "               slot):\n",
    "        \"\"\" Update channel matrix every coherence_time slots \"\"\"\n",
    "        # Generate new channel realization\n",
    "        h_freq_new = self.call(channel_model)\n",
    "\n",
    "        # Change to new channel every coherence_time slots\n",
    "        change = tf.cast(tf.math.mod(\n",
    "            slot, self.coherence_time) == 0, self.cdtype)\n",
    "        h_freq = change * h_freq_new + \\\n",
    "            (tf.cast(1, self.cdtype) - change) * h_freq\n",
    "        return h_freq\n",
    "\n",
    "    def apply_fading(self,\n",
    "                     h_freq):\n",
    "        \"\"\" Apply fading, modeled as an autoregressive process, to channel matrix \"\"\"\n",
    "        # Multiplicative fading factor evolving via an AR process\n",
    "        # [batch_size, num_rx, num_tx]\n",
    "        self.fading = tf.cast(1, self.rdtype) - self.rho_fading + self.rho_fading * self.fading + \\\n",
    "            config.tf_rng.uniform(\n",
    "                self.fading.shape, minval=-.1, maxval=.1, dtype=self.rdtype)\n",
    "        self.fading = tf.maximum(self.fading, tf.cast(0, self.rdtype))\n",
    "        # [batch_size, num_rx, 1, num_tx, 1, 1, 1]\n",
    "        fading_expand = insert_dims(self.fading, 1, axis=2)\n",
    "        fading_expand = insert_dims(fading_expand, 3, axis=4)\n",
    "\n",
    "        # Channel matrix in the current slot\n",
    "        h_freq_fading = tf.cast(tf.math.sqrt(\n",
    "            fading_expand), self.cdtype) * h_freq\n",
    "        return h_freq_fading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User stream management\n",
    "\n",
    "The\n",
    "[StreamManagement](https://nvlabs.github.io/sionna/phy/api/mimo.html#stream-management)\n",
    "class configures which base station serves which user.  \n",
    "We assume here for\n",
    "simplicity that each receiver is associated with the nearest base station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.794799Z",
     "iopub.status.busy": "2025-03-14T11:09:22.794546Z",
     "iopub.status.idle": "2025-03-14T11:09:22.801225Z",
     "shell.execute_reply": "2025-03-14T11:09:22.800456Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_stream_management(direction,\n",
    "                          num_rx,\n",
    "                          num_tx,\n",
    "                          num_streams_per_ut,\n",
    "                          num_ut_per_sector):\n",
    "    \"\"\"\n",
    "    Instantiate a StreamManagement object.\n",
    "    It determines which data streams are intended for each receiver\n",
    "    \"\"\"\n",
    "    if direction == 'downlink':\n",
    "        num_streams_per_tx = num_streams_per_ut * num_ut_per_sector\n",
    "        # RX-TX association matrix\n",
    "        rx_tx_association = np.zeros([num_rx, num_tx])\n",
    "        idx = np.array([[i1, i2] for i2 in range(num_tx) for i1 in\n",
    "                        np.arange(i2*num_ut_per_sector,\n",
    "                                  (i2+1)*num_ut_per_sector)])\n",
    "        rx_tx_association[idx[:, 0], idx[:, 1]] = 1\n",
    "\n",
    "    else:\n",
    "        num_streams_per_tx = num_streams_per_ut\n",
    "        # RX-TX association matrix\n",
    "        rx_tx_association = np.zeros([num_rx, num_tx])\n",
    "        idx = np.array([[i1, i2] for i1 in range(num_rx) for i2 in\n",
    "                        np.arange(i1*num_ut_per_sector,\n",
    "                                  (i1+1)*num_ut_per_sector)])\n",
    "        rx_tx_association[idx[:, 0], idx[:, 1]] = 1\n",
    "\n",
    "    stream_management = StreamManagement(\n",
    "        rx_tx_association, num_streams_per_tx)\n",
    "    return stream_management"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SINR computation\n",
    "\n",
    "Computing the signal-to-noise-plus-interference-ratio (SINR) is\n",
    "the first step to determine the block error rate (BLER) and, eventually, the user\n",
    "throughput, via the physical layer abstraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.805006Z",
     "iopub.status.busy": "2025-03-14T11:09:22.804758Z",
     "iopub.status.idle": "2025-03-14T11:09:22.813116Z",
     "shell.execute_reply": "2025-03-14T11:09:22.812248Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_sinr(tx_power,\n",
    "             stream_management,\n",
    "             no,\n",
    "             direction,\n",
    "             h_freq_fading,\n",
    "             num_bs,\n",
    "             num_ut_per_sector,\n",
    "             num_streams_per_ut,\n",
    "             resource_grid):\n",
    "    \"\"\" Compute post-equalization SINR. It is assumed:\n",
    "     - DL: Regularized zero-forcing precoding\n",
    "     - UL: No precoding, only power allocation\n",
    "    LMMSE equalizer is used in both DL and UL.\n",
    "    \"\"\"\n",
    "    # tx_power: [batch_size, num_bs, num_tx_per_sector,\n",
    "    #            num_streams_per_tx, num_ofdm_sym, num_subcarriers]\n",
    "    # Flatten across sectors\n",
    "    # [batch_size, num_tx, num_streams_per_tx, num_ofdm_symbols, num_subcarriers]\n",
    "    s = tx_power.shape\n",
    "    tx_power = tf.reshape(tx_power, [s[0], s[1]*s[2]] + s[3:])\n",
    "\n",
    "    # Compute SINR\n",
    "    # [batch_size, num_ofdm_sym, num_subcarriers, num_ut,\n",
    "    #  num_streams_per_ut]\n",
    "    if direction == 'downlink':\n",
    "        # Regularized zero-forcing precoding in the DL\n",
    "        precoded_channel = RZFPrecodedChannel(resource_grid=resource_grid,\n",
    "                                              stream_management=stream_management)\n",
    "        h_eff = precoded_channel(h_freq_fading,\n",
    "                                 tx_power=tx_power,\n",
    "                                 alpha=no)  # Regularizer\n",
    "    else:\n",
    "        # No precoding in the UL: just power allocation\n",
    "        precoded_channel = EyePrecodedChannel(resource_grid=resource_grid,\n",
    "                                              stream_management=stream_management)\n",
    "        h_eff = precoded_channel(h_freq_fading,\n",
    "                                 tx_power=tx_power)\n",
    "\n",
    "    # LMMSE equalizer\n",
    "    lmmse_posteq_sinr = LMMSEPostEqualizationSINR(resource_grid=resource_grid,\n",
    "                                                  stream_management=stream_management)\n",
    "    # Post-equalization SINR\n",
    "    # [batch_size, num_ofdm_symbols, num_subcarriers, num_rx, num_streams_per_rx]\n",
    "    sinr = lmmse_posteq_sinr(h_eff, no=no, interference_whitening=True)\n",
    "\n",
    "    # [batch_size, num_ofdm_symbols, num_subcarriers, num_ut, num_streams_per_ut]\n",
    "    sinr = tf.reshape(\n",
    "        sinr, sinr.shape[:-2] + [num_bs*num_ut_per_sector, num_streams_per_ut])\n",
    "\n",
    "    # Regroup by sector\n",
    "    # [batch_size, num_ofdm_symbols, num_subcarriers, num_bs, num_ut_per_sector, num_streams_per_ut]\n",
    "    sinr = tf.reshape(\n",
    "        sinr, sinr.shape[:-2] + [num_bs, num_ut_per_sector, num_streams_per_ut])\n",
    "\n",
    "    # [batch_size, num_bs, num_ofdm_sym, num_subcarriers, num_ut_per_sector, num_streams_per_ut]\n",
    "    sinr = tf.transpose(sinr, [0, 3, 1, 2, 4, 5])\n",
    "    return sinr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation of Achievable Rate\n",
    "\n",
    "To fairly allocate users to the appropriate time and frequency resources, the\n",
    "proportional fairness (PF) scheduler must first estimate the *achievable* rate for\n",
    "each user.\n",
    "\n",
    "Users are then ranked based on the ratio of their achievable rate to\n",
    "their *achieved* rate, called PF metric.  \n",
    "\n",
    "Here, we assume that the achievable rate is estimated using the Shannon capacity\n",
    "based on the effective SINR.  \n",
    "The rate estimation is thus uniform across subcarriers, resulting in only one\n",
    "user being scheduled per slot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.816254Z",
     "iopub.status.busy": "2025-03-14T11:09:22.815985Z",
     "iopub.status.idle": "2025-03-14T11:09:22.821034Z",
     "shell.execute_reply": "2025-03-14T11:09:22.820290Z"
    }
   },
   "outputs": [],
   "source": [
    "def estimate_achievable_rate(sinr_eff_db_last,\n",
    "                             num_ofdm_sym,\n",
    "                             num_subcarriers):\n",
    "    \"\"\" Estimate achievable rate \"\"\"\n",
    "    # [batch_size, num_bs, num_ut_per_sector]\n",
    "    rate_achievable_est = log2(tf.cast(1, sinr_eff_db_last.dtype) +\n",
    "                               db_to_lin(sinr_eff_db_last))\n",
    "\n",
    "    # Broadcast to time/frequency grid\n",
    "    # [batch_size, num_bs, num_ofdm_sym, num_subcarriers, num_ut_per_sector]\n",
    "    rate_achievable_est = insert_dims(\n",
    "        rate_achievable_est, 2, axis=-2)\n",
    "    rate_achievable_est = tf.tile(rate_achievable_est,\n",
    "                                  [1, 1, num_ofdm_sym, num_subcarriers, 1])\n",
    "    return rate_achievable_est"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result recording\n",
    "\n",
    "We record the relevant metrics in a dictionary for further analysis, including:\n",
    "- Number of decoded bits;\n",
    "- HARQ feedback;\n",
    "- MCS index;\n",
    "- Effective SINR;\n",
    "- Pathloss of serving cell;\n",
    "- Number of allocated REs;\n",
    "- Proportional fairness (PF) metric;\n",
    "- Transmit power;\n",
    "- Outer-loop link adaptation (OLLA) offset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.824941Z",
     "iopub.status.busy": "2025-03-14T11:09:22.824625Z",
     "iopub.status.idle": "2025-03-14T11:09:22.837603Z",
     "shell.execute_reply": "2025-03-14T11:09:22.836947Z"
    }
   },
   "outputs": [],
   "source": [
    "def init_result_history(batch_size,\n",
    "                        num_slots,\n",
    "                        num_bs,\n",
    "                        num_ut_per_sector):\n",
    "    \"\"\" Initialize dictionary containing history of results \"\"\"\n",
    "    hist = {}\n",
    "    for key in ['pathloss_serving_cell',\n",
    "                'tx_power', 'olla_offset',\n",
    "                'sinr_eff', 'pf_metric',\n",
    "                'num_decoded_bits', 'mcs_index',\n",
    "                'harq', 'num_allocated_re']:\n",
    "        hist[key] = tf.TensorArray(\n",
    "            size=num_slots,\n",
    "            element_shape=[batch_size,\n",
    "                           num_bs,\n",
    "                           num_ut_per_sector],\n",
    "            dtype=tf.float32)\n",
    "    return hist\n",
    "\n",
    "\n",
    "def record_results(hist,\n",
    "                   slot,\n",
    "                   sim_failed=False,\n",
    "                   pathloss_serving_cell=None,\n",
    "                   num_allocated_re=None,\n",
    "                   tx_power_per_ut=None,\n",
    "                   num_decoded_bits=None,\n",
    "                   mcs_index=None,\n",
    "                   harq_feedback=None,\n",
    "                   olla_offset=None,\n",
    "                   sinr_eff=None,\n",
    "                   pf_metric=None,\n",
    "                   shape=None):\n",
    "    \"\"\" Record results of last slot \"\"\"\n",
    "    if not sim_failed:\n",
    "        for key, value in zip(['pathloss_serving_cell', 'olla_offset', 'sinr_eff',\n",
    "                               'num_allocated_re', 'tx_power', 'num_decoded_bits',\n",
    "                               'mcs_index', 'harq'],\n",
    "                              [pathloss_serving_cell, olla_offset, sinr_eff,\n",
    "                               num_allocated_re, tx_power_per_ut, num_decoded_bits,\n",
    "                               mcs_index, harq_feedback]):\n",
    "            hist[key] = hist[key].write(slot, tf.cast(value, tf.float32))\n",
    "        # Average PF metric across resources\n",
    "        hist['pf_metric'] = hist['pf_metric'].write(\n",
    "            slot, tf.reduce_mean(pf_metric, axis=[-2, -3]))\n",
    "    else:\n",
    "        nan_tensor = tf.cast(tf.fill(shape,\n",
    "                                     float('nan')), dtype=tf.float32)\n",
    "        for key in hist:\n",
    "            hist[key] = hist[key].write(slot, nan_tensor)\n",
    "    return hist\n",
    "\n",
    "\n",
    "def clean_hist(hist, batch=0):\n",
    "    \"\"\" Extract batch, convert to Numpy, and mask metrics when user is not\n",
    "    scheduled \"\"\"\n",
    "    # Extract batch and convert to Numpy\n",
    "    for key in hist:\n",
    "        try:\n",
    "            # [num_slots, num_bs, num_ut_per_sector]\n",
    "            hist[key] = hist[key].numpy()[:, batch, :, :]\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    # Mask metrics when user is not scheduled\n",
    "    hist['mcs_index'] = np.where(\n",
    "        hist['harq'] == -1, np.nan, hist['mcs_index'])\n",
    "    hist['sinr_eff'] = np.where(\n",
    "        hist['harq'] == -1, np.nan, hist['sinr_eff'])\n",
    "    hist['tx_power'] = np.where(\n",
    "        hist['harq'] == -1, np.nan, hist['tx_power'])\n",
    "    hist['num_allocated_re'] = np.where(\n",
    "        hist['harq'] == -1, 0, hist['num_allocated_re'])\n",
    "    hist['harq'] = np.where(\n",
    "        hist['harq'] == -1, np.nan, hist['harq'])\n",
    "    return hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "\n",
    "We now put everything together and create a system-level simulator Sionna block.\n",
    "We will then demonstrate how to instantiate it and run simulations. \n",
    "\n",
    "### Sionna block\n",
    "\n",
    "We next define a compound Sionna block called `SystemLevelSimulator` composed of multiple submodules.  \n",
    "It is structured as follows:\n",
    "- `__init__` method, which initializes the main modules, including:\n",
    "    - 3GPP channel model;\n",
    "    - Multicell topology and user drop\n",
    "    - Stream management;\n",
    "    - Physical layer abstraction;\n",
    "    - Scheduler;\n",
    "    - Link adaptation;\n",
    "- `call` method, which loops over slots and performs:\n",
    "    - Channel generation;\n",
    "    - Channel estimation, assumed ideal;\n",
    "    - User scheduling;\n",
    "    - Power control;\n",
    "    - Per-stream SINR computation;\n",
    "    - Physical layer abstraction;\n",
    "    - User mobility.\n",
    "\n",
    "To enable scaling up to tens of sectors and hundreds of users, we shall use\n",
    "XLA compilation by decorating the `call` method by `@tf.function(jit_compile=True)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.842223Z",
     "iopub.status.busy": "2025-03-14T11:09:22.841993Z",
     "iopub.status.idle": "2025-03-14T11:09:22.875370Z",
     "shell.execute_reply": "2025-03-14T11:09:22.874558Z"
    }
   },
   "outputs": [],
   "source": [
    "class SystemLevelSimulator(Block):\n",
    "    def __init__(self,\n",
    "                 batch_size,\n",
    "                 num_rings,\n",
    "                 num_ut_per_sector,\n",
    "                 carrier_frequency,\n",
    "                 resource_grid,\n",
    "                 scenario,\n",
    "                 direction,\n",
    "                 ut_array,\n",
    "                 bs_array,\n",
    "                 bs_max_power_dbm,\n",
    "                 ut_max_power_dbm,\n",
    "                 coherence_time,\n",
    "                 pf_beta=0.98,\n",
    "                 max_bs_ut_dist=None,\n",
    "                 min_bs_ut_dist=None,\n",
    "                 temperature=294,\n",
    "                 o2i_model='low',\n",
    "                 average_street_width=20.0,\n",
    "                 average_building_height=5.0,\n",
    "                 precision=None):\n",
    "        super().__init__(precision=precision)\n",
    "\n",
    "        assert scenario in ['umi', 'uma', 'rma']\n",
    "        assert direction in ['uplink', 'downlink']\n",
    "        self.scenario = scenario\n",
    "        self.batch_size = int(batch_size)\n",
    "        self.resource_grid = resource_grid\n",
    "        self.num_ut_per_sector = int(num_ut_per_sector)\n",
    "        self.direction = direction\n",
    "        self.bs_max_power_dbm = bs_max_power_dbm  # [dBm]\n",
    "        self.ut_max_power_dbm = ut_max_power_dbm  # [dBm]\n",
    "        self.coherence_time = tf.cast(coherence_time, tf.int32)  # [slots]\n",
    "        num_cells = get_num_hex_in_grid(num_rings)\n",
    "        self.num_bs = num_cells * 3\n",
    "        self.num_ut = self.num_bs * self.num_ut_per_sector\n",
    "        self.num_ut_ant = ut_array.num_ant\n",
    "        self.num_bs_ant = bs_array.num_ant\n",
    "        if bs_array.polarization == 'dual':\n",
    "            self.num_bs_ant *= 2\n",
    "        if self.direction == 'uplink':\n",
    "            self.num_tx, self.num_rx = self.num_ut, self.num_bs\n",
    "            self.num_tx_ant, self.num_rx_ant = self.num_ut_ant, self.num_bs_ant\n",
    "            self.num_tx_per_sector = self.num_ut_per_sector\n",
    "        else:\n",
    "            self.num_tx, self.num_rx = self.num_bs, self.num_ut\n",
    "            self.num_tx_ant, self.num_rx_ant = self.num_bs_ant, self.num_ut_ant\n",
    "            self.num_tx_per_sector = 1\n",
    "\n",
    "        # Assume 1 stream for UT antenna\n",
    "        self.num_streams_per_ut = resource_grid.num_streams_per_tx\n",
    "\n",
    "        # Set TX-RX pairs via StreamManagement\n",
    "        self.stream_management = get_stream_management(direction,\n",
    "                                                       self.num_rx,\n",
    "                                                       self.num_tx,\n",
    "                                                       self.num_streams_per_ut,\n",
    "                                                       num_ut_per_sector)\n",
    "        # Noise power per subcarrier\n",
    "        self.no = tf.cast(BOLTZMANN_CONSTANT * temperature *\n",
    "                          resource_grid.subcarrier_spacing, self.rdtype)\n",
    "\n",
    "        # Slot duration [sec]\n",
    "        self.slot_duration = resource_grid.ofdm_symbol_duration * \\\n",
    "            resource_grid.num_ofdm_symbols\n",
    "\n",
    "        # Initialize channel model based on scenario\n",
    "        self._setup_channel_model(\n",
    "            scenario, carrier_frequency, o2i_model, ut_array, bs_array,\n",
    "            average_street_width, average_building_height)\n",
    "\n",
    "        # Generate multicell topology\n",
    "        self._setup_topology(num_rings, min_bs_ut_dist, max_bs_ut_dist)\n",
    "\n",
    "        # Instantiate a PHY abstraction object\n",
    "        self.phy_abs = PHYAbstraction(precision=self.precision)\n",
    "\n",
    "        # Instantiate a link adaptation object\n",
    "        self.olla = OuterLoopLinkAdaptation(\n",
    "            self.phy_abs,\n",
    "            self.num_ut_per_sector,\n",
    "            batch_size=[self.batch_size, self.num_bs])\n",
    "\n",
    "        # Instantiate a scheduler object\n",
    "        self.scheduler = PFSchedulerSUMIMO(\n",
    "            self.num_ut_per_sector,\n",
    "            resource_grid.fft_size,\n",
    "            resource_grid.num_ofdm_symbols,\n",
    "            batch_size=[self.batch_size, self.num_bs],\n",
    "            num_streams_per_ut=self.num_streams_per_ut,\n",
    "            beta=pf_beta,\n",
    "            precision=self.precision)\n",
    "\n",
    "    def _setup_channel_model(self, scenario, carrier_frequency, o2i_model,\n",
    "                             ut_array, bs_array, average_street_width,\n",
    "                             average_building_height):\n",
    "        \"\"\" Initialize appropriate channel model based on scenario \"\"\"\n",
    "        common_params = {\n",
    "            'carrier_frequency': carrier_frequency,\n",
    "            'ut_array': ut_array,\n",
    "            'bs_array': bs_array,\n",
    "            'direction': self.direction,\n",
    "            'enable_pathloss': True,\n",
    "            'enable_shadow_fading': True,\n",
    "            'precision': self.precision\n",
    "        }\n",
    "\n",
    "        if scenario == 'umi':  # Urban micro-cell\n",
    "            self.channel_model = UMi(o2i_model=o2i_model, **common_params)\n",
    "        elif scenario == 'uma':  # Urban macro-cell\n",
    "            self.channel_model = UMa(o2i_model=o2i_model, **common_params)\n",
    "        elif scenario == 'rma':  # Rural macro-cell\n",
    "            self.channel_model = RMa(\n",
    "                average_street_width=average_street_width,\n",
    "                average_building_height=average_building_height,\n",
    "                **common_params)\n",
    "\n",
    "    def _setup_topology(self, num_rings, min_bs_ut_dist, max_bs_ut_dist):\n",
    "        \"\"\"G enerate and set up network topology \"\"\"\n",
    "        self.ut_loc, self.bs_loc, self.ut_orientations, self.bs_orientations, \\\n",
    "            self.ut_velocities, self.in_state, self.los, self.bs_virtual_loc, self.grid = \\\n",
    "            gen_hexgrid_topology(\n",
    "                batch_size=self.batch_size,\n",
    "                num_rings=num_rings,\n",
    "                num_ut_per_sector=self.num_ut_per_sector,\n",
    "                min_bs_ut_dist=min_bs_ut_dist,\n",
    "                max_bs_ut_dist=max_bs_ut_dist,\n",
    "                scenario=self.scenario,\n",
    "                los=True,\n",
    "                return_grid=True,\n",
    "                precision=self.precision)\n",
    "\n",
    "        # Set topology in channel model\n",
    "        self.channel_model.set_topology(\n",
    "            self.ut_loc, self.bs_loc, self.ut_orientations,\n",
    "            self.bs_orientations, self.ut_velocities,\n",
    "            self.in_state, self.los, self.bs_virtual_loc)\n",
    "\n",
    "    def _reset(self,\n",
    "               bler_target,\n",
    "               olla_delta_up):\n",
    "        \"\"\"  Reset OLLA and HARQ/SINR feedback \"\"\"\n",
    "        # Link Adaptation\n",
    "        self.olla.reset()\n",
    "        self.olla.bler_target = bler_target\n",
    "        self.olla.olla_delta_up = olla_delta_up\n",
    "\n",
    "        # HARQ feedback (no feedback, -1)\n",
    "        last_harq_feedback = - tf.ones(\n",
    "            [self.batch_size, self.num_bs, self.num_ut_per_sector],\n",
    "            dtype=tf.int32)\n",
    "\n",
    "        # SINR feedback\n",
    "        sinr_eff_feedback = tf.ones(\n",
    "            [self.batch_size, self.num_bs, self.num_ut_per_sector],\n",
    "            dtype=self.rdtype)\n",
    "\n",
    "        # N. decoded bits\n",
    "        num_decoded_bits = tf.zeros(\n",
    "            [self.batch_size, self.num_bs, self.num_ut_per_sector],\n",
    "            tf.int32)\n",
    "        return last_harq_feedback, sinr_eff_feedback, num_decoded_bits\n",
    "\n",
    "    def _group_by_sector(self,\n",
    "                         tensor):\n",
    "        \"\"\" Group tensor by sector\n",
    "        - Input: [batch_size, num_ut, num_ofdm_symbols]\n",
    "        - Output: [batch_size, num_bs, num_ofdm_symbols, num_ut_per_sector]\n",
    "        \"\"\"\n",
    "        tensor = tf.reshape(tensor, [self.batch_size,\n",
    "                                     self.num_bs,\n",
    "                                     self.num_ut_per_sector,\n",
    "                                     self.resource_grid.num_ofdm_symbols])\n",
    "        # [batch_size, num_bs, num_ofdm_symbols, num_ut_per_sector]\n",
    "        return tf.transpose(tensor, [0, 1, 3, 2])\n",
    "\n",
    "    @tf.function(jit_compile=True)\n",
    "    def call(self,\n",
    "             num_slots,\n",
    "             alpha_ul,\n",
    "             p0_dbm_ul,\n",
    "             bler_target,\n",
    "             olla_delta_up,\n",
    "             mcs_table_index=1,\n",
    "             fairness_dl=0,\n",
    "             guaranteed_power_ratio_dl=0.5):\n",
    "\n",
    "        # -------------- #\n",
    "        # Initialization #\n",
    "        # -------------- #\n",
    "        # Initialize result history\n",
    "        hist = init_result_history(self.batch_size,\n",
    "                                   num_slots,\n",
    "                                   self.num_bs,\n",
    "                                   self.num_ut_per_sector)\n",
    "\n",
    "        # Reset OLLA and HARQ/SINR feedback\n",
    "        last_harq_feedback, sinr_eff_feedback, num_decoded_bits = \\\n",
    "            self._reset(bler_target, olla_delta_up)\n",
    "\n",
    "        # Initialize channel matrix\n",
    "        self.channel_matrix = ChannelMatrix(self.resource_grid,\n",
    "                                            self.batch_size,\n",
    "                                            self.num_rx,\n",
    "                                            self.num_tx,\n",
    "                                            self.coherence_time,\n",
    "                                            precision=self.precision)\n",
    "        # [batch_size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_ofdm_sym,\n",
    "        #  num_subcarriers]\n",
    "        h_freq = self.channel_matrix(self.channel_model)\n",
    "\n",
    "        # --------------- #\n",
    "        # Simulate a slot #\n",
    "        # --------------- #\n",
    "        def simulate_slot(slot,\n",
    "                          hist,\n",
    "                          harq_feedback,\n",
    "                          sinr_eff_feedback,\n",
    "                          num_decoded_bits,\n",
    "                          h_freq):\n",
    "            try:\n",
    "                # ------- #\n",
    "                # Channel #\n",
    "                # ------- #\n",
    "                # Update channel matrix\n",
    "                h_freq = self.channel_matrix.update(self.channel_model,\n",
    "                                                    h_freq,\n",
    "                                                    slot)\n",
    "\n",
    "                # Apply fading\n",
    "                h_freq_fading = self.channel_matrix.apply_fading(h_freq)\n",
    "\n",
    "                # --------- #\n",
    "                # Scheduler #\n",
    "                # --------- #\n",
    "                # Estimate achievable rate\n",
    "                # [batch_size, num_bs, num_ofdm_sym, num_subcarriers, num_ut_per_sector]\n",
    "                rate_achievable_est = estimate_achievable_rate(\n",
    "                    self.olla.sinr_eff_db_last,\n",
    "                    self.resource_grid.num_ofdm_symbols,\n",
    "                    self.resource_grid.fft_size)\n",
    "\n",
    "                # SU-MIMO Proportional Fairness scheduler\n",
    "                # [batch_size, num_bs, num_ofdm_sym, num_subcarriers,\n",
    "                #  num_ut_per_sector, num_streams_per_ut]\n",
    "                is_scheduled = self.scheduler(\n",
    "                    num_decoded_bits,\n",
    "                    rate_achievable_est)\n",
    "\n",
    "                # N. allocated subcarriers\n",
    "                num_allocated_sc = tf.minimum(tf.reduce_sum(\n",
    "                    tf.cast(is_scheduled, tf.int32), axis=-1), 1)\n",
    "                # [batch_size, num_bs, num_ofdm_sym, num_ut_per_sector]\n",
    "                num_allocated_sc = tf.reduce_sum(\n",
    "                    num_allocated_sc, axis=-2)\n",
    "\n",
    "                # N. allocated resources per slot\n",
    "                # [batch_size, num_bs, num_ut_per_sector]\n",
    "                num_allocated_re = \\\n",
    "                    tf.reduce_sum(tf.cast(is_scheduled, tf.int32),\n",
    "                                  axis=[-1, -3, -4])\n",
    "\n",
    "                # ------------- #\n",
    "                # Power control #\n",
    "                # ------------- #\n",
    "                # Compute pathloss\n",
    "                # [batch_size, num_rx, num_tx, num_ofdm_symbols], [batch_size, num_ut, num_ofdm_symbols]\n",
    "                pathloss_all_pairs, pathloss_serving_cell = get_pathloss(\n",
    "                    h_freq_fading,\n",
    "                    rx_tx_association=tf.convert_to_tensor(\n",
    "                        self.stream_management.rx_tx_association))\n",
    "                # Group by sector\n",
    "                # [batch_size, num_bs, num_ofdm_symbols, num_ut_per_sector]\n",
    "                pathloss_serving_cell = self._group_by_sector(\n",
    "                    pathloss_serving_cell)\n",
    "\n",
    "                if self.direction == 'uplink':\n",
    "                    # Open-loop uplink power control\n",
    "                    # [batch_size, num_bs, num_ofdm_symbols, num_ut_per_sector]\n",
    "                    tx_power_per_ut = open_loop_uplink_power_control(\n",
    "                        pathloss_serving_cell,\n",
    "                        num_allocated_sc,\n",
    "                        alpha=alpha_ul,\n",
    "                        p0_dbm=p0_dbm_ul,\n",
    "                        ut_max_power_dbm=self.ut_max_power_dbm)\n",
    "                else:\n",
    "                    # Channel quality estimation:\n",
    "                    # Estimate interference from neighboring base stations\n",
    "                    # [batch_size, num_ut, num_ofdm_symbols]\n",
    "\n",
    "                    one = tf.cast(1, pathloss_serving_cell.dtype)\n",
    "\n",
    "                    # Total received power\n",
    "                    # [batch_size, num_ut, num_ofdm_symbols]\n",
    "                    rx_power_tot = tf.reduce_sum(\n",
    "                        one / pathloss_all_pairs, axis=-2)\n",
    "                    # [batch_size, num_bs, num_ut_per_sector, num_ofdm_symbols]\n",
    "                    rx_power_tot = self._group_by_sector(rx_power_tot)\n",
    "\n",
    "                    # Interference from neighboring base stations\n",
    "                    interference_dl = rx_power_tot - one / pathloss_serving_cell\n",
    "                    interference_dl *= dbm_to_watt(self.bs_max_power_dbm)\n",
    "\n",
    "                    # Fair downlink power allocation\n",
    "                    # [batch_size, num_bs, num_ofdm_symbols, num_ut_per_sector]\n",
    "                    tx_power_per_ut, _ = downlink_fair_power_control(\n",
    "                        pathloss_serving_cell,\n",
    "                        interference_dl + self.no,\n",
    "                        num_allocated_sc,\n",
    "                        bs_max_power_dbm=self.bs_max_power_dbm,\n",
    "                        guaranteed_power_ratio=guaranteed_power_ratio_dl,\n",
    "                        fairness=fairness_dl,\n",
    "                        precision=self.precision)\n",
    "\n",
    "                # For each user, distribute the power uniformly across\n",
    "                # subcarriers and streams\n",
    "                # [batch_size, num_bs, num_tx_per_sector,\n",
    "                #  num_streams_per_tx, num_ofdm_sym, num_subcarriers]\n",
    "                tx_power = spread_across_subcarriers(\n",
    "                    tx_power_per_ut,\n",
    "                    is_scheduled,\n",
    "                    num_tx=self.num_tx_per_sector,\n",
    "                    precision=self.precision)\n",
    "\n",
    "                # --------------- #\n",
    "                # Per-stream SINR #\n",
    "                # --------------- #\n",
    "                # [batch_size, num_bs, num_ofdm_sym, num_subcarriers,\n",
    "                #  num_ut_per_sector, num_streams_per_ut]\n",
    "                sinr = get_sinr(tx_power,\n",
    "                                self.stream_management,\n",
    "                                self.no,\n",
    "                                self.direction,\n",
    "                                h_freq_fading,\n",
    "                                self.num_bs,\n",
    "                                self.num_ut_per_sector,\n",
    "                                self.num_streams_per_ut,\n",
    "                                self.resource_grid)\n",
    "\n",
    "                # --------------- #\n",
    "                # Link adaptation #\n",
    "                # --------------- #\n",
    "                # [batch_size, num_bs, num_ut_per_sector]\n",
    "                mcs_index = self.olla(num_allocated_re,\n",
    "                                      harq_feedback=harq_feedback,\n",
    "                                      sinr_eff=sinr_eff_feedback)\n",
    "\n",
    "                # --------------- #\n",
    "                # PHY abstraction #\n",
    "                # --------------- #\n",
    "                # [batch_size, num_bs, num_ut_per_sector]\n",
    "                num_decoded_bits, harq_feedback, sinr_eff, _, _ = self.phy_abs(\n",
    "                    mcs_index,\n",
    "                    sinr=sinr,\n",
    "                    mcs_table_index=mcs_table_index,\n",
    "                    mcs_category=int(self.direction == 'downlink'))\n",
    "\n",
    "                # ------------- #\n",
    "                # SINR feedback #\n",
    "                # ------------- #\n",
    "                # [batch_size, num_bs, num_ut_per_sector]\n",
    "                sinr_eff_feedback = tf.where(num_allocated_re > 0,\n",
    "                                             sinr_eff,\n",
    "                                             tf.cast(0., self.rdtype))\n",
    "\n",
    "                # Record results\n",
    "                hist = record_results(hist,\n",
    "                                      slot,\n",
    "                                      sim_failed=False,\n",
    "                                      pathloss_serving_cell=tf.reduce_sum(\n",
    "                                          pathloss_serving_cell, axis=-2),\n",
    "                                      num_allocated_re=num_allocated_re,\n",
    "                                      tx_power_per_ut=tf.reduce_sum(\n",
    "                                          tx_power_per_ut, axis=-2),\n",
    "                                      num_decoded_bits=num_decoded_bits,\n",
    "                                      mcs_index=mcs_index,\n",
    "                                      harq_feedback=harq_feedback,\n",
    "                                      olla_offset=self.olla.offset,\n",
    "                                      sinr_eff=sinr_eff,\n",
    "                                      pf_metric=self.scheduler.pf_metric)\n",
    "\n",
    "            except tf.errors.InvalidArgumentError as e:\n",
    "                print(f\"SINR computation did not succeed at slot {slot}.\\n\"\n",
    "                      f\"Error message: {e}. Skipping slot...\")\n",
    "                hist = record_results(hist, slot,\n",
    "                                      shape=[self.batch_size,\n",
    "                                             self.num_bs,\n",
    "                                             self.num_ut_per_sector], sim_failed=True)\n",
    "\n",
    "            # ------------- #\n",
    "            # User mobility #\n",
    "            # ------------- #\n",
    "            self.ut_loc = self.ut_loc + self.ut_velocities * self.slot_duration\n",
    "\n",
    "            # Set topology in channel model\n",
    "            self.channel_model.set_topology(\n",
    "                self.ut_loc, self.bs_loc, self.ut_orientations,\n",
    "                self.bs_orientations, self.ut_velocities,\n",
    "                self.in_state, self.los, self.bs_virtual_loc)\n",
    "\n",
    "            return [slot + 1, hist, harq_feedback, sinr_eff_feedback,\n",
    "                    num_decoded_bits, h_freq]\n",
    "\n",
    "        # --------------- #\n",
    "        # Simulation loop #\n",
    "        # --------------- #\n",
    "        _, hist, *_ = tf.while_loop(\n",
    "            lambda i, *_: i < num_slots,\n",
    "            simulate_slot,\n",
    "            [0, hist, last_harq_feedback, sinr_eff_feedback,\n",
    "             num_decoded_bits, h_freq])\n",
    "\n",
    "        for key in hist:\n",
    "            hist[key] = hist[key].stack()\n",
    "        return hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario parameters\n",
    "\n",
    "To initialize the system-level simulator, we must define the following parameters:\n",
    "- 3GPP desidered scenario and the multicell grid;\n",
    "- Communication direction (downlink or uplink);\n",
    "- OFDM resource grid configuration;\n",
    "- Modulation and coding scheme table;\n",
    "- Transmit power for the base stations and user terminals;\n",
    "- Antenna arrays at the base stations and user terminals.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.879199Z",
     "iopub.status.busy": "2025-03-14T11:09:22.878978Z",
     "iopub.status.idle": "2025-03-14T11:09:22.883732Z",
     "shell.execute_reply": "2025-03-14T11:09:22.882884Z"
    }
   },
   "outputs": [],
   "source": [
    "# Communication direction\n",
    "direction = 'downlink'  # 'uplink' or 'downlink'\n",
    "\n",
    "# 3GPP scenario parameters\n",
    "scenario = 'umi'  # 'umi', 'uma' or 'rma'\n",
    "\n",
    "# Number of rings of the hexagonal grid\n",
    "# With num_rings=1, 7*3=21 base stations are placed\n",
    "num_rings = 1\n",
    "\n",
    "# N. users per sector\n",
    "num_ut_per_sector = 10\n",
    "\n",
    "# Max/min distance between base station and served users\n",
    "max_bs_ut_dist = 80  # [m]\n",
    "min_bs_ut_dist = 0  # [m]\n",
    "\n",
    "# Carrier frequency\n",
    "carrier_frequency = 3.5e9  # [Hz]\n",
    "\n",
    "# Transmit power for base station and user terminals\n",
    "bs_max_power_dbm = 56  # [dBm]\n",
    "ut_max_power_dbm = 26  # [dBm]\n",
    "\n",
    "# Channel is regenerated every coherence_time slots\n",
    "coherence_time = 100  # [slots]\n",
    "\n",
    "# MCS table index\n",
    "# Ranges within [1;4] for downlink and [1;2] for uplink, as in TS 38.214\n",
    "mcs_table_index = 1\n",
    "\n",
    "# Number of examples\n",
    "batch_size = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.887512Z",
     "iopub.status.busy": "2025-03-14T11:09:22.887262Z",
     "iopub.status.idle": "2025-03-14T11:09:22.932175Z",
     "shell.execute_reply": "2025-03-14T11:09:22.930736Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the antenna arrays at the base stations\n",
    "bs_array = PanelArray(num_rows_per_panel=2,\n",
    "                      num_cols_per_panel=3,\n",
    "                      polarization='dual',\n",
    "                      polarization_type='VH',\n",
    "                      antenna_pattern='38.901',\n",
    "                      carrier_frequency=carrier_frequency)\n",
    "\n",
    "# Create the antenna array at the user terminals\n",
    "ut_array = PanelArray(num_rows_per_panel=1,\n",
    "                      num_cols_per_panel=1,\n",
    "                      polarization='single',\n",
    "                      polarization_type='V',\n",
    "                      antenna_pattern='omni',\n",
    "                      carrier_frequency=carrier_frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.936606Z",
     "iopub.status.busy": "2025-03-14T11:09:22.935777Z",
     "iopub.status.idle": "2025-03-14T11:09:22.959380Z",
     "shell.execute_reply": "2025-03-14T11:09:22.958582Z"
    }
   },
   "outputs": [],
   "source": [
    "# n. OFDM symbols, i.e., time samples, in a slot\n",
    "num_ofdm_sym = 1\n",
    "# N. available subcarriers\n",
    "num_subcarriers = 128\n",
    "# Subcarrier spacing, i.e., bandwitdh width of each subcarrier\n",
    "subcarrier_spacing = 15e3  # [Hz]\n",
    "\n",
    "# Create the OFDM resource grid\n",
    "resource_grid = ResourceGrid(num_ofdm_symbols=num_ofdm_sym,\n",
    "                             fft_size=num_subcarriers,\n",
    "                             subcarrier_spacing=subcarrier_spacing,\n",
    "                             num_tx=num_ut_per_sector,\n",
    "                             num_streams_per_tx=ut_array.num_ant)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulator initialization\n",
    "\n",
    "We can now initialize the `SystemLevelSimulator` block by providing the\n",
    "scenario parameters as inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:22.962809Z",
     "iopub.status.busy": "2025-03-14T11:09:22.962468Z",
     "iopub.status.idle": "2025-03-14T11:09:27.868341Z",
     "shell.execute_reply": "2025-03-14T11:09:27.867526Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize SYS object\n",
    "sls = SystemLevelSimulator(\n",
    "    batch_size,\n",
    "    num_rings,\n",
    "    num_ut_per_sector,\n",
    "    carrier_frequency,\n",
    "    resource_grid,\n",
    "    scenario,\n",
    "    direction,\n",
    "    ut_array,\n",
    "    bs_array,\n",
    "    bs_max_power_dbm,\n",
    "    ut_max_power_dbm,\n",
    "    coherence_time,\n",
    "    max_bs_ut_dist=max_bs_ut_dist,\n",
    "    min_bs_ut_dist=min_bs_ut_dist,\n",
    "    temperature=294,  # Environment temperature for noise power computation\n",
    "    o2i_model='low',  # 'low' or 'high',\n",
    "    average_street_width=20.,\n",
    "    average_building_height=10.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize below the multicell topology we just created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:27.872328Z",
     "iopub.status.busy": "2025-03-14T11:09:27.872125Z",
     "iopub.status.idle": "2025-03-14T11:09:28.121013Z",
     "shell.execute_reply": "2025-03-14T11:09:28.120336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sls.grid.show()\n",
    "ax = fig.get_axes()\n",
    "ax[0].plot(sls.ut_loc[0, :, 0], sls.ut_loc[0, :, 1],\n",
    "           'xk', label='user position')\n",
    "ax[0].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that:\n",
    "\n",
    "- The base stations are placed on a hexagonal spiral grid with the defined\n",
    "  number of rings;\n",
    "- The inter-site distance depends on the chosen 3GPP scenario;\n",
    "- Users are dropped uniformly at random within each sector, within the maximum\n",
    "  distance from the respective base station. \n",
    "\n",
    "**Remark**: Hand-over procedures are **not**\n",
    "implemented in this notebook; users simply attach to the nearest base station.  \n",
    "As a result, user located near the cell edge may experience high interference.  \n",
    "To mitigate this, the parameter `max_bs_ut_dist` can be adjusted to limit the\n",
    "maximum distance between a user and its serving base station."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Layer-2 parameters\n",
    "\n",
    "Next, we define the parameters associated with the link\n",
    "adaptation and power control modules, such as:\n",
    "- BLER target, typically set to 10%;\n",
    "- `olla_delta_up`, which determines how quickly outer-loop link\n",
    "  adaptation (OLLA) adjusts the MCS index to the varying channel conditions;\n",
    "- `(alpha_ul,p0_dbm_ul)`, defining the\n",
    "  pathloss compensation factor and the target received power at the base\n",
    "  station, respectively, in open-loop uplink power control;\n",
    "\n",
    "as well as the number of slots to simulate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:28.124071Z",
     "iopub.status.busy": "2025-03-14T11:09:28.123764Z",
     "iopub.status.idle": "2025-03-14T11:09:28.128461Z",
     "shell.execute_reply": "2025-03-14T11:09:28.127857Z"
    }
   },
   "outputs": [],
   "source": [
    "# N. slots to simulate\n",
    "num_slots = tf.constant(1000, tf.int32)\n",
    "\n",
    "# Link Adaptation\n",
    "bler_target = tf.constant(0.1, tf.float32)  # Must be in [0, 1]\n",
    "olla_delta_up = tf.constant(0.2, tf.float32)\n",
    "\n",
    "# Uplink power control parameters\n",
    "# Pathloss compensation factor\n",
    "alpha_ul = tf.constant(1., tf.float32)  # Must be in [0, 1]\n",
    "# Target received power at the base station\n",
    "p0_dbm_ul = tf.constant(-80., tf.float32)  # [dBm]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation\n",
    "\n",
    "We are now finally ready to run system-level simulations on the defined 3GPP\n",
    "multicell scenario!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:09:28.131275Z",
     "iopub.status.busy": "2025-03-14T11:09:28.130973Z",
     "iopub.status.idle": "2025-03-14T11:10:36.682533Z",
     "shell.execute_reply": "2025-03-14T11:10:36.681633Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-18 16:08:08.280558: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'loop_add_fusion_8', 4 bytes spill stores, 4 bytes spill loads\n",
      "ptxas warning : Registers are spilled to local memory in function 'input_concatenate_fusion_8', 8 bytes spill stores, 8 bytes spill loads\n",
      "ptxas warning : Registers are spilled to local memory in function 'input_transpose_fusion_3', 16 bytes spill stores, 16 bytes spill loads\n",
      "ptxas warning : Registers are spilled to local memory in function 'loop_reduce_fusion_15', 8 bytes spill stores, 84 bytes spill loads\n",
      "ptxas warning : Registers are spilled to local memory in function 'input_concatenate_fusion_25', 8 bytes spill stores, 8 bytes spill loads\n",
      "ptxas warning : Registers are spilled to local memory in function 'input_transpose_fusion_15', 16 bytes spill stores, 16 bytes spill loads\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# System-level simulations\n",
    "hist = sls(num_slots,\n",
    "           alpha_ul,\n",
    "           p0_dbm_ul,\n",
    "           bler_target,\n",
    "           olla_delta_up)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance metric analysis\n",
    "\n",
    "In this last section, we extract insights from simulations by analyzing the output data\n",
    "at the user granularity. \n",
    "\n",
    "First, we average the relevant metrics across slots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:10:36.685944Z",
     "iopub.status.busy": "2025-03-14T11:10:36.685744Z",
     "iopub.status.idle": "2025-03-14T11:10:36.701962Z",
     "shell.execute_reply": "2025-03-14T11:10:36.701197Z"
    }
   },
   "outputs": [],
   "source": [
    "hist = clean_hist(hist)\n",
    "\n",
    "# Average across slots and store in dictionary\n",
    "results_avg = {\n",
    "    'TBLER': (1 - np.nanmean(hist['harq'], axis=0)).flatten(),\n",
    "    'MCS': np.nanmean(hist['mcs_index'], axis=0).flatten(),\n",
    "    '# decoded bits / slot': np.nanmean(hist['num_decoded_bits'], axis=0).flatten(),\n",
    "    'Effective SINR [dB]': 10*np.log10(np.nanmean(hist['sinr_eff'], axis=0).flatten()),\n",
    "    'OLLA offset': np.nanmean(hist['olla_offset'], axis=0).flatten(),\n",
    "    'TX power [dBm]': 10*np.log10(np.nanmean(hist['tx_power'], axis=0).flatten()) + 30,\n",
    "    'Pathloss [dB]': 10*np.log10(np.nanmean(hist['pathloss_serving_cell'], axis=0).flatten()),\n",
    "    '# allocated REs / slot': np.nanmean(hist['num_allocated_re'], axis=0).flatten(),\n",
    "    'PF metric': np.nanmean(hist['pf_metric'], axis=0).flatten()\n",
    "}\n",
    "metrics = list(results_avg.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A bird's eye view\n",
    "\n",
    "We now visualize the cumulative density function (CDF) of the selected\n",
    "metrics, averaged across slots and computed per user, including:\n",
    "- Transport block error rate (TBLER): Indicates how well link adaptation maintained it near the target value.\n",
    "- MCS index;\n",
    "- Number of decoded bits per slot;\n",
    "- Effective SINR: Measures the channel quality for each user;\n",
    "- Outer-loop link adaptation (OLLA) offset: Reflects the extent to which OLLA corrected the SINR estimation;\n",
    "- Transmit power;\n",
    "- Pathloss from the serving cell;\n",
    "- Number of allocated resource elements (RE);\n",
    "- Proportional fairness (PF) metric: A lower value indicates a fairer resource allocation across users.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:10:36.705519Z",
     "iopub.status.busy": "2025-03-14T11:10:36.705321Z",
     "iopub.status.idle": "2025-03-14T11:10:37.392161Z",
     "shell.execute_reply": "2025-03-14T11:10:37.391516Z"
    }
   },
   "outputs": [
    {
     "data": {
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zZ6J37944ffo04uPjlZYpKSmBi4sLnn/+efj6+sLExATHjx/HxYsX8dVXXwGoOXv7008/YdSoUejSpQtmzJgBZ2dn3L9/HydPnoSZmRn++uuvFrWDVCrF8OHDMXHiRMTFxeG7777DgAED8MwzzwCoKZQ2bNiAV155Bb169cLkyZNha2uLtLQ0HDx4EP3796/3C3Rj2NraIiwsDEuWLMHIkSPxzDPPcDH06dNH6YqQqvTr1w+WlpaYNm0a3nnnHfB4PPzyyy9NvrXpYVOnTsXPP/+M0NBQXLhwAQMHDkRZWRmOHz+Ot956C+PGjcPgwYPxxhtvYPny5YiJiUFQUBCEQiESEhKwZ88erF27Fs8//3wrbqmyDz74AH/++SfGjh2L6dOnw8/PD2VlZbh+/Tp+//13pKSkwMbGBoaGhujcuTN27dqFDh06wMrKCl27dkXXrl2b9HlDhw7FK6+8gm+++QYJCQkYOXIkFAoF/vnnHwwdOhQhISFYunQpTp8+jTFjxsDd3R3Z2dn47rvv4OLi8thnshBCyJNQYUEIUbn58+ejQ4cO+Prrr7kzqa6urggKCuK+WDfHwoULkZOTg99//x27d+/GqFGjcPjwYaWOukZGRnjrrbdw7NgxboSq9u3b47vvvlMaZWfIkCGIiorCsmXLsG7dOpSWlsLBwQEBAQF44403mr/xD6xbtw7bt2/HwoULIZPJMGXKFHzzzTdKt8C8+OKLcHJywhdffIGVK1eiqqoKzs7OGDhwYItHylq8eDFsbW2xbt06vPfee7CyssLrr7+Ozz//XC1Pb7a2tsbff/+N//3vf/jkk09gaWmJl19+GcOHD0dwcHCz1ikQCHDo0CF89tln2LFjB/744w9YW1tjwIAB6NatG7fcxo0b4efnh++//x4fffQRDAwM4OHhgZdffhn9+/dvrU2sl5GREU6dOoXPP/8ce/bswc8//wwzMzN06NABS5YsURph66effsLbb7+N9957D1KpFIsWLWpyYQEAW7ZsQffu3bFp0yZ88MEHMDc3R+/evdGvXz8AwDPPPIOUlBRs3rwZubm5sLGxweDBg+vEQwghTcVjLTldRAgh5LFqH8x28eLFBju4E0IIIfqA+lgQQgghhBBCWowKC0IIIYQQQkiLUWFBCCGEEEIIaTHqY0EIIYQQQghpMbpiQQghhBBCCGkxKiwIIYQQQgghLUaFBSGEEEIIIaTFqLAghBBCCCGEtBgVFoQQQgghhJAWo8KCEEIIIYQQ0mJUWBBCCCGEEEJajAoLQgghhBBCSItRYUEIIYQQQghpMSosCCGEEEIIIS1GhQUhhBBCCCGkxaiwIIQQQgghhLQYFRaEEEIIIYSQFqPCghBCCCGEENJiVFgQQgghhBBCWowKC0IIIYQQQkiLUWFBCCGEEEIIaTEqLAghhBBCCCEtRoUFIYQQQgghpMWosCCEEEIIIYS0GBUWhBBCCCGEkBYz0HQA2kihUCA9PR2mpqbg8XiaDoeQRmOMoaSkBE5OTuDz6bxBa6B8QHQZ5YTWRfmA6DJ15AMqLOqRnp4OV1dXTYdBSLPdvXsXLi4umg5DL1A+IPqAckLroHxA9IEq8wEVFvUwNTUFUNPwZmZm3HSZTIZjx44hKCgIQqFQU+HpDWrP1iWTybB//37MnDmT24dJyzWUD/QBHYPK9LE9iouL4erqSjmhlTwuH+jj/tNYbXXbdW271ZIPmAadOnWKjR07ljk6OjIAbN++fU98z8mTJ1nPnj2ZSCRiXl5ebMuWLXWWWbduHXN3d2disZj5+/uz8+fPNymuoqIiBoAVFRUpTZdKpWz//v1MKpU2aX2kftSerUsqlbIdO3bUu+/qAl3LB/qAjkFl+tgeurz/amNOeFx76uP+01htddt1bbvVkQ80esNlWVkZfH19sX79+kYtn5ycjDFjxmDo0KGIiYnB3LlzMXPmTBw9epRbZteuXQgNDcWiRYtw+fJl+Pr6Ijg4GNnZ2araDEJIK6B8QAh5GOUEQnSQykqWJkIjzkbMmzePdenSRWnapEmTWHBwMPfa39+fzZkzh3stl8uZk5MTW758eaNjoSsW6kHt2bp0/YrFw3QhH+gDOgaV6WN76Mv+qy05ga5Y1K+tbruubbc68oFO9bGIiopCYGCg0rTg4GDMnTsXACCVShEdHY2wsDBuPp/PR2BgIKKiohpcb1VVFaqqqrjXxcXFAGrunZPJZNz02v8/PI00H7Vn052Iy8GRm1mY9pQbujjVvb+3LdF0PtAHdAwq04X2YIwhv0yKxJwy3MktQ1JOGXzsTfCCX/0dMbV5W1qbKnJCU/KBLuw/qtJWt13d210pk6O4shpFFTKUPPi3uLIaxQ/9KzLg438jvB8bryrpVGGRmZkJe3t7pWn29vYoLi5GRUUFCgoKIJfL610mNja2wfUuX74cS5YsqTP92LFjMDIyqjM9PDy8ybErFArcu3cPAODi4kLD/j2kOe3ZFqWWAlviBCiQ8lCSdQ9Puys0HZJGaUs+0Ad0DCrTdHvIGZBXCWRX8JBXBeRX8ZD/4N+8SqBCrjzMaRdLBYyzrtW7rvLycnWErBVUkROakw80vf9oUlvd9uZsd2U1kPvguC6TARVyoLyah4pqoLy65nVFNe/BdKCiGqhmTx7i2ETI0EmWUO88deQDnSosVCUsLAyhoaHc69pe80FBQXVGhQoPD8eIESOa3Pu/rKwMlpaWAICCggIYGxu3TvA6rCXt2Zbkl0lx4GoG1p6Ph1zBYCYxwMIX+8HRXKK0nEwmw4EDBzQUpf5obD7QB3QMKtNke1RVK7DyWDzOJuUhJa8cMjlrcFkeD3C2MISXrTHa2xrD18Uco7o61Lts7Rl20jxNyQdt+Xhqq9velO1WKBjWn7qDyLgc3C2oQEF5864e8HmAmUQIU4kBzA2FMJMYwOyhf62MhRg9wLPe96ojH+hUYeHg4ICsrCylaVlZWTAzM4OhoSEEAgEEAkG9yzg41J90AUAsFkMsFteZLhQK691RGpr+OA8v35z36zNqj/rlllZh7fEE7Lp4F1J5zdWJHq4W+O6lXnCyMNRwdJqnLflAH+jztjWHOtsjp6QKN+4XYcvZFJyOz+GmS4R8tLMxgYeNEVwsjeBsYQgXS0M4WxrC3coYhiJBo9bfln6vqsgJzckHbfl4aqvb/vB2F5RJEX47C4nZpcgoqkRmUQUyiipxr6CizvusjEVwtTSEtYmYKxLMDYU1hYKhEGYS4YPXNdPNDYUwFhmAz2/ewxnV8bvRqcKib9++OHTokNK08PBw9O3bFwAgEong5+eHiIgIjB8/HkDNLUgREREICQlRd7iENNut9GJM+fEciipqzmh0cTLD075OeOUpdxiLdeqwVRnKB0RXXU4rwB/R93AiNhsZRZXcdJGAjw+CfTCyqwOcLQyb/eWhraKcQDQpJbcMK47G4ujNLMgV9V9xFBvwMbWvOyb0coGrlRFM9PDvuUa3qLS0FImJidzr5ORkxMTEwMrKCm5ubggLC8P9+/fx888/AwDefPNNrFu3DvPmzcOrr76KEydOYPfu3Th48CC3jtDQUEybNg29e/eGv78/1qxZg7KyMsyYMUPt20dIc236NxlFFTK4WRnhy+e6o6+XtaZDUjnKB6Qt+O1CGsL2Xude83hAOxtj+LpY4LWBnujiZK7B6LQL5QSiK67cLcT0rdEol8oBAJ0dzfBUO2s4WUjgaG4IB3MJHM0lsDUVQyjQ7z62Gi0sLl26hKFDh3Kva+9jnDZtGrZu3YqMjAykpaVx8z09PXHw4EG89957WLt2LVxcXPDTTz8hODiYW2bSpEnIycnBwoULkZmZiR49euDIkSN1OmsRoo1i7hbi13Op+ONyTUf/BWM7t4miAqB8QPRXaVU1Dl/PwP6Y+ziTmAcAMBQKsPEVP/i5W+rlWcvWQDmBaLN7BeXYf/kejsXxcS3qAgDAz90Snz3bFR0d9Ks/XlNoNJsNGTIEjDXcQW3r1q31vufKlSuPXW9ISAhd1iQ6Jz6rBOPXn+FeP9XOCkN9bDUYkXpRPiD6plquwNK/b2H3pbuolP03itvTvk74ZEwn2JtJHvNuQjmBaJO80irEZZYgOa8MqXnl+OH0nQdzaq5AmBsK8d1Lvdr8cU2nSQjRkEqZHBeS83E6PgenE3IQn1XKzftt1lPw97SCgO6xJkRnVFXLkZJbjoTsEtxML8afMem4X1jTYbOdjTGe7emM8T2d4Wqln8MWE6KP7uaXY+vZFGw9m1Jv34lAJwWmj/RHLw9rGInoazW1gJoIhUK8//773P9J25VfJsXX4fHYE618FpPPA4b42GHh2M7wsKHhiAnRdowx5JTWjOq07kQirt4rqvPFw0gkwKfju+LZns7g8ehEASG6QqFg+OD3a9ytyQDgYW0ETxtjeNgYw9PGGL1czJB0+R8EeFpBKKSv1AAVFmojEomwcuVKTYdBNKygTIoXfzyH2MwSAICDmQSDOthgUAdbDGhvAwsjkYYjJIQ8zuW0Avx6LhUJWaVIzi1DaVW10nxTsQG87U3Q3s4E/dvbYERnezqLSYiOKZdWI2THFZyIzQYADGhvg9cGeGJoRzul5WQyGZI0EaAWo2xHiJpIqxWYsOEsknPLIBHy8cMrvTHQ24bOYhKiI76JSMDq8HilaXwe4GxpiKE+dnh9UDs4WxjSMU2IjlIoGK7cLcSXh2NxISUfIgM+VjzXHeN7Oms6NJ1BhYWaKBQKbvQKNzc38Pn6PdwYUVYtV+D9PVeRnFsGANj1el/4ulpoNihCSKNtP5/KFRWdHM3wXqA32tkaw9XKCGKDxj2sjhCifbKKK3E6Pgfnk/PxT0IOsoqrANQ8c2LLjD7o52Wj4Qh1CxUWalJRUQFPz5pHrJeWlsLYmO6hbwtOxmVj0z/JiE4tQIWsZnzrlc93p6KCEB2QU1KFfxNzcPh6Jo7dqnlac//21tjwsh/MJNRXjhBddyWtAJN/OIeq6v/6O5qIDRDYyQ5zAztQf8dmoMKCEBXJKq5E6K4YFJTXPD3bVGKAt4e1xwu9XTUcGSGkIVI5sOVsKg7eyMLVu4VK88Z0d8S6KT3pVidCdFxVtRxrjyfgu8iaHhJ8HvDGYC/0bWeNgHZWdBWyBaiwIKQZGGNIyy9HZlEl8sqkNT+lVcgvkyKvVIrskkpcSStE9YMRYvbP6Y/uzubg0/CxhGidSpkctzOKcfVuAVZeFqCsOo6b18XJDIM62OLZns7oYG+qwSgJIc2lUDCcT87Hn1fv43JqIZJySrm/z3amYuyb0x/OFoYajlI/UGFBSBNkF1fi6K0s7L54F9fvFz1xeV8Xcywd15VufSJEi+SWVuHwjUzEpBXixv0iJOaUPjRMbE3x/9YQL0zv5wG7Nv6wK0J0GWMMidmlWHjgJqLu5CnNM5MYoIebJVY81x0O5nSctxYqLAhphOziSny8/wbCH9xnDQBCAQ8ulkawNhbBylgEaxMxrI1FsDaped3FyQzt7egMJyHaolxajSV/3sIfl+9xZytrWRuL0MXJFOLyHEwN8sdAH3sNRUkIaYnYzGL8m5CLC8n5uJiSz92ODAAv+LkguIsDOjuZwdFcQrc1qgAVFoQ8gULBMOuXaO5+655uFhjd1RHP+bnAypieO0GItqmUyZFTUoXskirklFQiu6QK2cVVCL+VhbismmfIdHcxx1AfO3RzNkdXZ3PYm4lRXV2NQ4cO4al2VhreAkJIUykUDJ8duo1N/yYrTZcI+ejpaonp/T0Q3MVBQ9G1HVRYEPIEK4/F4erdQgj4PPwxux960G1NhGiViyn5OB2fg1vpxbidUYz0osoGl7UxEePbKT3R18tajRESQlqLQsFQXClDfpkUBeVS5JfJkJRTij9j0nEroxgAMNDbBv3b26CPhxW6OZtDZEBD/KsLFRZqYmBggLfeeov7P9F+jNV09tr84OzHl891p6KCEC1RLq3GxZQC/H01HXui79WZLxLwYWsqhp2ZGHamYtiZSuBkYYjnejlTvwlCdARjDBG3s7Hp32Rkl1SioFyGwnIpHrmTkWPA52HF890xoZeLegMlHPqGqyZisRjr16/XdBikkX6PvocfTichPqsUABDgaYXnetGTNwnRlHJpNY7cyMTZpDxcv1eEhOwSpS8X/h5WGNPdEZ0czeBtZwILIyHdP02IjkkvrMDFlHzE3C1EbEYJ4rJKkF8mrXdZU4kBLI1EsDQWwc5UjEHeNgjq4gB7OnGgUVRYEPKQ0qpqfHbwNn67UPOUdJGAj+f8XDB/VEf6kkKIBkirFdh4Kgk/nL6D0qpqpXlO5hIM9LbF0I52CO5iT8coITrqZnoR3vntCpJyyurM4/OAMd2dMMXfFdbGYlgaC2FhKKLbm7QUFRZqwhhDbm4uAMDGxob+AGqp5Yf+KypmDfREyDBvmBvSE3YJUReFguF+YQWSckpxJ6cMuy7e5Tpcu1kZYWx3R/R0s0R3F3M6M0mIjmOM4cd/7mB1eDwqZQrweEA3Z3P0crNEFyczdHQwg7e9CSRCemCdrqDCQk3Ky8thZ2cHACgtLYWxMT0mXpswxvBdZBJXVHw4siNmD/HScFSEtB0KBcOtjGLM+vkSMh7pfM3n1fRxeq6XCz1kkhA9kVNShaV/38JfV9MBAH3bWWPtlB6wM6UTBrqMCguil6qq5Th3Jx+F5VKUVFajrKrmp6Sq9v9ylFZVo/TB6/TCChRX1txmMcXfFW8ObqfhLSCkbUjNK8OqY/GIjMtGSeV/tzp525mgna0xvGxNENjZHr3cLDUYJSGkNZRLq3EhuaYPxbazKdwzJsJGdcTrg9rR3Rx6gAoLonf+vpaOZX/fQlZxVZPex+cBn4zpjBn9PSi5EaIGJ+Oy8davl1EhkwMADIUC+Lqa443BXhjqY6fh6AghrYUxhk3/JmPdyUQUPvTAOlOJAba96k8nDvQIFRZEb/x1NR07L6bhTGIeAMDOVAxvexMYiwxgIjaAicQAxuIH/xfX/l8AE7EQxmIBHM0N4WBOl2AJUaVKmRzht7Kw78p9nIjNBgD4e1ohbFRHdHM2h4GAOmQSoi8qpHIcu5WJ3y6k4dydfAA1gy4EtLNGTzcLBHayh5OFoYajJK2JCgui06qq5TgVl4M90fcQfiuLmz61rzs+HtMJYgPq8EWINlAoGFYdi8MvUakoeWh0p1eecscnY+lYJUSfXL9XhF/PpeLg9QxuNDc+D/hodCfM6O8JAfWV0ltUWBCdFrr7Kg5ey+BeWxmLsO+tfnC3ps7xhGiTk3HZ+C4yCQDgbGGIZ3s649lezvCyNdFwZISQ1iBTAPuupGP7xXu4ereQm+5qZYhnezhjQi8XeNjQ32Z9pxXXnNevXw8PDw9IJBIEBATgwoULDS47ZMgQ8Hi8Oj9jxozhlpk+fXqd+SNHjlTHphA1Yozh/INLq37ullg7uQf+mTeUigodR/lAPxRVyHAqPgeb/k1G2N5rWLD/BgBgej8P/DNvKN4P9qGigjwR5QPdEJ1agEXRAszbewNX7xZCKOBhXA8n7Hr9KZz+YChCg3yoqGgjNH7FYteuXQgNDcXGjRsREBCANWvWIDg4GHFxcdzwrA/bu3cvpNL/nsKYl5cHX19fvPDCC0rLjRw5Elu2bOFei8Vi1W1EIxgYGGDatGnc/0nLVMsVWH44FrmlVTASCbDtVX+YiKlddV1byQf6Kru4En9eTcexW1mITi2A/OFHYwMwEgnw5mAvGjKWNArlA92QmF2C2TtiUFbNg4nYALOHeGFib1fYmlK7tkUa/ya2evVqzJo1CzNmzAAAbNy4EQcPHsTmzZsxf/78OstbWVkpvd65cyeMjIzqJA6xWAwHBwfVBd5EYrEYW7du1XQYeuGP6HtYdzIRybk1T+gMG92Jigo90VbygT7ae/ke3t9zFQ/XEh7WRtwDrtrbmcDP3ZIGSCCNRvlA+8RlliAqKRfpRZW4X1CB+4UViHlw25OpkOHY3P5wtKQrkW2ZRr+NSaVSREdHIywsjJvG5/MRGBiIqKioRq1j06ZNmDx5cp0HzkVGRsLOzg6WlpYYNmwYPv30U1hbW9e7jqqqKlRV/Tc0aXFxMQBAJpNBJvtvWLTa/z88jTRfc9pzW1QqPj0UBwAwNzTAkqc7Y0w3B/qdQPf3S13LB/qgtXJaTkkVPtp3nSsqFozpiKE+NnC1NGrwM7WRPuZ4Xd0WXcwH+rj/PCwyPgezt8eg+pErkQAgEvAx00cKczFfb7e/Prr2O1dHnBotLHJzcyGXy2Fvb6803d7eHrGxsU98/4ULF3Djxg1s2rRJafrIkSMxYcIEeHp6IikpCR999BFGjRqFqKgoCAR1Rx5Zvnw5lixZUmf6sWPHYGRU9w9jeHj4E2N7FGOMS05isZiek/CQxrZnYhHwfawAAA9P2SkwwaMSvLuXceiuauMj6qGr+UAfNCenAQBjwNV8Hn5N5EOmqMlpL7eXwyb/Bq5HAddbM0g1am57aKPy8nJNh9AsupwP9Gn/qVVeDSyPEaBawYO1mKGbFYOlmMFSBFiKGWwkgJGBfm57Y+jKdqsjH+j0/SObNm1Ct27d4O/vrzR98uTJ3P+7deuG7t27w8vLC5GRkRg+fHid9YSFhSE0NJR7XVxcDFdXVwQFBcHMzIybLpPJEB4ejhEjRkAoFDYp1rKyMlha1jwApqCgoM4ZlLaoMe0pVzAk55ZhfeQd/H0rE0DN7RXfv/EUjEQ6vfu2OplMhgMHDmg6DI1Rdz7QB03JadJqBVLzypGYU4rEnDIkZZchNqsESTk1tyR2djTF9L7uGOfrqLN9KFqS47VV7Rn2tkYT+UAf959aXxyJQ7EsFSZiA+x/px8czJRvadTnbX8cXdtudeQDjX4zs7GxgUAgQFZWltL0rKysJ97/WFZWhp07d2Lp0qVP/Jx27drBxsYGiYmJ9SYOsVhcb+ctoVBY747S0PTHeXj55rxfnz3aHpUyOZJySvHF4VhcTMlHpUzBzRvXwwkLxnaGuTF1CtM3upoP9EF921Ypk+PwjQwcvZGFhOwSpOSV1+mMDQBiAz5m9PfE/4I6QKgnD7fTp9+1rm6HLucDfdp/0gsrcD45D7su3QcAfDKmE1ytTRtcXp+2vSl0ZbvVEaNGCwuRSAQ/Pz9ERERg/PjxAACFQoGIiAiEhIQ89r179uxBVVUVXn755Sd+zr1795CXlwdHR8fWCJs0k0LBkF1ShfuF5bhXUIG03FKcu8PH3ztikF8uQ25pFXJLqlAmlSu9z1AoQEdHUwzuYIt3h3vTbWR6ivKBdmCM4dydfPxvdwzSiyqV5pmIDdDezgTedjWdsb3tTeDrYgFrEyr0SeuifKBZjDEsPHATv5xL5aZZGAkR2Nn+Me8iRAtuhQoNDcW0adPQu3dv+Pv7Y82aNSgrK+NGgZg6dSqcnZ2xfPlypfdt2rQJ48ePr9PhqrS0FEuWLMFzzz0HBwcHJCUlYd68eWjfvj2Cg4PVtl3kPzK5AtvOpuDr8Pg6RQPAB7Ky67xHJODDyUKC0CAfjOnmSE/pbCMoH2hWfpkUM7ddxOW0QgCAvZkYU/zd4OduCW87U9ibUf8woj6UDzTn56hU/HIuFTwe0N3ZHAHtrDGpjyts6CQCeQKNFxaTJk1CTk4OFi5ciMzMTPTo0QNHjhzhOmylpaWBz1e+vB4XF4d///0Xx44dq7M+gUCAa9euYdu2bSgsLISTkxOCgoKwbNkyGqtazWRyBT47eBt7L99DcWU1N93F0hDOFoZwMhejLOc++vXsDHtzI1ibiGFjIoKNqRimYgP6AtMGUT7QHIWCYeGBG1xRMbmPKz4e0wmmEu2/vE/0E+UDzbibX47lh28DAD4e3QkzB7bTcEREl2i8sACAkJCQBi9tRkZG1pnm4+MDxure6wsAhoaGOHr0aGuGR5roZnoRfj2XhvBbmcgtrXlYkY2JCJP6uOK9wA4weHAftkwmw6FDdzE6wE0n7k0k6kH5QL0UDLiUWoCf/k1FRGw2+Dzgp2m9Mawj3fJANI/ygXpVSOWY9/s1VMoUCPC0wmsDPDUdEtExWlFYEP2QW1qFlUfisDv6LmrzuqnYAIuf6YLxPZ3pdiZCtEhOSRW+iYjHvmgBSs9dBADwecDK532pqCCkjVp++Dai7uRBbMDHp+O70p0DpMmosFATgUCA559/nvu/PpErGE7H5+CT/Tdwv7ACADCmuyNe8HPBU+2sIRHq1/YSosvuF1Zg7fF4/HU1AxUyOQAeTMQGGNnVAa8PaocO9g2P+EII0V8/R6Xg56iaztqrJ/aAN+UC0gxUWKiJRCLBnj17NB1GqymXVmP7uTScu5OHS6kFKKqoeZqjs4Uh1k7ugd4eVhqOkBDysNKqahyIuY/Vx+KRV1Zzi2I3ZzP0My3AO5MCYWxI95gT0hYxxvD3tQwsPHATAPD6oHYY3e3xQ/oS0hAqLEiTMcbw+s/R+Dcxl5tmYSTE+B7OeHOwFxzMJY95NyFE3RKzS/Hq1otIy6956qqNiRjfTukJP1dTHD58GCID/Xj+BCGk8Rhj+OH0Hey6eBd3cmsedDnF3w1hozrSLVCk2aiwII1WVlWNtREJ+DMmHZnFleDzgPcCO2BQB1t0djLTm4djEaIvbtwvwpdHYvFPwn8nAT4c2REv9HaBjYkYMplMg9ERQjTpZnoxlh+OBVAzxPuLAW74eEwnKipIi1BhoSZlZWUwMTEBUDOWtrGxsYYjejLGGP5NzEV0agFiM0pwNimXGzZWZMBH2KiOmNGfRowgRBvJFQxTfjiHkqpq8HnAEB87vD2sPXq6WWo6NEKIFvjySE1R4WVrjP1z+tPQ0qRVUGFBGnQgJh1zd8UoTTMVG2Dh050xupsjjMW0+xCirRKzS1FSVXMi4MT/hsDDRvtPZhBCVEuhYNhxIQ07L6bhxv1i8HnApml9qKggrYa+GRIlBWVSbDyVhFPxOYjNLAEAWBoJMWdoe3RyNENXZ3OYG1ICIkRbMcaQlFOKWT9fAgD0dLOgooIQAgBYdzIRq8Pjudcv+LlSfiCtigoLwom5W4j391xFYnYpN+3Zns747NmuMBLRrkKINjt+Kwu/nk/FlbRCbpQ2AHh3uLcGoyKEaAOZXIFVR+Pw/ek7AICpfd3xxmAvOFsYajgyom/o2yLBjftF+Do8HhGx2QAAsQEfi5/pgqDO9rA2oSEoCdEmlTI5/rh8Dym5ZbhXUPHgpxwF5f8VE2IDPvw9rfDWkPbo62WtwWgJIZoWc7cQYXuv43ZGMQBg3kgfzB7sRZ20iUpQYdFGlVTK8Ou5NBy5mYmrdwsB1Dx199meLnh3uDfcrI00GyAhpF5vbb+MEw9OAjzMgM/DqwM8Mba7Izo50ihthLR1jDGsPBqHDaeSwFjNsPCfju+Ksd2dNB0a0WNUWLRRK4/GcU/Y5PGAcb5OeGe4N9rZmmg4MkJIQ36OSsGJ2GwIBTy88pQHXK0M4WJpBBdLQ7haGcGEBlQgpM2TKxi+O5lYc2Uzr+bZNRN6OePj0Z3oLgSicvRXSE0EAgFGjx7N/V8TGGNIyC7F6fgcbD+fBgCYG+iNF/3dYGdGD7UjRJtdTMnHoj9rnoz7zjBvvE19Jwgh9dh4KglfPeigLRLwMW+kD2YObKfhqEhbQYWFmkgkEhw8eFAjn11QJsW2qBT8diENWcVV3HQ/d0vMGdqebpkgRIsxxnD0Zhbe33MVjAHBXewRMqy9psMihGiZogoZ5v9xDYdvZAIAJvR0xpJxXWgoWaJWTfpGOXXqVJSUlHCvr169Sk9u1XJRSXkIXH0Ka44nIKu4ChIhHwO9bbBwbGf8NuspKipIs1E+UD25guGD36/hzV+jUVpVDUdzCcJG0ZNxifahfKBZuaVVmP1rNFdU+Hta4ZOxnamoIGrXpG+V27dvR0VFBfd64MCBuHv3bqsHRVpHcaUMb/4ajbwyKbztTPDNlJ64uigIv7wWgFcHeEJkQEUFaT7KB6pVVlWNT/Zfx+/R9yDg8zBnqBci/jeYxpwnWonygWYwxrD+ZCL6f3ECZ5PyAADvB3XA7jf6wspYpOHoSFvUpFuhGGOPfU0aVlZWBjs7OwBAdnY2jI1V9+VAJlcgMi4H+67cQ1GFDG5WRvjr7QGQCDXTt4PoJ8oHqjXv92s4eD0DALD4mS545Sl3DUdESMMoH2jG5jMpWHk0DgDg62KOD4I7YoC3jYajIm0Z9bFQo/LycpV/xo37RXhn5xXcySnjpj3Xy4WKCkJ0QO0AC79dSOOKikVPd6aighBSR7VcgfUnEwEA80d1xBuD2tFtkkTjmlxY3Lp1C5mZNffwMcYQGxuL0tJSpWW6d+/eOtGRJqmWK/DGL9G4X1gBK2MRnu7uiGGd7DGwPZ29IKpB+aB1MMaw/XwafvrnDjc8JFBzUmBGf08NRkZI41E+UK9fz6Uiv0wKCyMhZg7wpKKCaIUmFxbDhw9XusQ5duxYAACPxwNjDDweD3K5vPUiJE9UXCnDvwm5+CP6Hu4XVsBQKMDx0MF0fyVROcoHLVdWVY35e6/jr6vpAACRAR8D29vg5b7uGOxtq+HoCGk8ygfqE34rC4v/ugUA6OZsDgMaiIVoiSYVFsnJyaqKgzTD/iv3sfNiGi6lFKBa8V8y/19QByoqiMpRPmgZuYJh/5X7WHcyEcm5ZTDg8/B+sA9eecodxvSgO6JjKB+oz52cUoTtvQ4A6OVmgVUv+Go4IkL+06S/Xu7uqrnPd/369Vi5ciUyMzPh6+uLb7/9Fv7+/vUuu3XrVsyYMUNpmlgsRmVlJfeaMYZFixbhxx9/RGFhIfr3748NGzbA21t/HiiVU1KF93bHoPbkUDtbYwz1scOorg7o7WGl2eBIm6CqfNBWfHbwNjafqfkyZm8mxvoXe9GxS3QW5QP1eXdnDHJLq+Bjb4ot0/1hbkRDyhLt0axrZwkJCVi1ahVCQkLw9ttvY/Xq1bhz506zAti1axdCQ0OxaNEiXL58Gb6+vggODkZ2dnaD7zEzM0NGRgb3k5qaqjR/xYoV+Oabb7Bx40acP38exsbGCA4OVio+dFn4rSw8/e2/YAzwsDZC5PtDcOJ/Q7BgbGf6YkLUrjXzAVBzosHDwwMSiQQBAQG4cOFCg8tu3boVPB5P6UciUX6KPGMMCxcuhKOjIwwNDREYGIiEhIRmx9cart0rxJazNUXFe4EdcOy9wXTsEr1A+UC18sukuH6/CADw07TeVFQQ7cOa6PPPP2cGBgaMz+czBwcHZm9vz/h8PhMKhWzlypVNXR3z9/dnc+bM4V7L5XLm5OTEli9fXu/yW7ZsYebm5g2uT6FQMAcHB6VYCgsLmVgsZr/99lujYioqKmIAWFFRkdJ0qVTK9u/fz6RSaaPW87Dy8nI2ePBgNnjwYFZeXt7k9zPGWE5JJfvqaCxz//Bv5v7h36zroiPs76vpzVqXNmhJe5K6pFIp27FjR737rqq0dj7YuXMnE4lEbPPmzezmzZts1qxZzMLCgmVlZdW7/JYtW5iZmRnLyMjgfjIzM5WW+eKLL5i5uTnbv38/u3r1KnvmmWeYp6cnq6ioaFRMDeWDlnjzl0vM/cO/2Tu/XW61dTYHHYPK9LE9VLH/NqSt5wNV7z8KhYJ9sCeGuX/4Nxu26iRTKBQq+Zzm0MdjpzF0bbvVkQ+adCvUyZMn8cknn2DBggV49913YWlpCQDIz8/HmjVrMH/+fPj7+2PQoEGNWp9UKkV0dDTCwsK4aXw+H4GBgYiKimrwfaWlpXB3d4dCoUCvXr3w+eefo0uXLgBq7vPMzMxEYGAgt7y5uTkCAgIQFRWFyZMn11lfVVUVqqqquNfFxcUAAJlMpvTk0Nr/N+dpogYGBggPD6+zrsb65Vwavjwaj6pqBQCgq5MZdrzWB4Yigc4+3bQl7UnqUnc7tnY+AIDVq1dj1qxZ3O2OGzduxMGDB7F582bMnz+/3vfweDw4ODjUO48xhjVr1uCTTz7BuHHjAAA///wz7O3tsX///nrzgaol5ZTiTGIuAGBCLxe1fz4hqkD5QPV2XEjD7kv3AADLxnWlUaCIVmpSYbFx40bMnDkTixcvVppuZWWFpUuXIjMzExs2bGh04sjNzYVcLoe9vb3SdHt7e8TGxtb7Hh8fH2zevBndu3dHUVERVq1ahX79+uHmzZtwcXHhhrqrb5218x61fPlyLFmypM70Y8eOwcjIqM70hwsEVVEwILGYhyt5PMQV8pBXVZNAHAwZRrkq0N0qHyePH1V5HOqgjvYkra+184GunWhoqsziSry3+xoupRYCAIxEAnSyN9JoYU3FvTJ9bA91bQvlA9XvP1sf9MkKGdIOfdzNtWo/1cdjpzF0bbvVEWeTCosLFy7gl19+aXD+K6+8gqlTp7Y4qMfp27cv+vbty73u168fOnXqhO+//x7Lli1r1jrDwsIQGhrKvS4uLoarqyuCgoJgZmbGTZfJZAgPD8eIESMgFKruvsaErFKE7IzBndz/xrMX8HkY5G2N76b00Jth5dTVnm2FTCbDgQMH1PZ5rZ0PdPVEQ2NcyObh9xQ+quQ88HkMPuYMgc7ViIo83qz1tTYq7pXpU3uo48GsAOWDh6li/ymTAXdyBAB4MC2Ix6FD8a3+Ga1Bn46dptCV7VZHPmhSYZGVlQUPD48G53t6ejZ4cNbHxsYGAoEAWVlZdT6noUuZjxIKhejZsycSE2uePln7vqysLDg6Oiqts0ePHvWuQywWQywW17vu+r7wNjT9ccrKyri2S0lJgbGxcb3LMcbwzu5ruJNbDlOJAcZ2d0RgJ3v4e1rBVKKfX76b055E81o7HzSHJk80NMWXq06jSl6Jbs5m+PqF7nC3bl6B0tqouFemj+1Re4Zd1SgfqHb/WfjnLcjZPbhYGmLahAEQ8LXrNih9PHYaQ9e2Wx35oEmFRWVlJUSihp+PIBQKIZVKG70+kUgEPz8/REREYPz48QAAhUKBiIgIhISENGodcrkc169fx+jRowHUJC8HBwdERERwhURxcTHOnz+P2bNnNzo2VcjNzX3iMonZpUjMrnlSaUToYNiZSZ7wDkI0o7Xzga6eaHicarkCOy/eRXpRzYh0373kB1cr7SgqHkbFvTJ9ag91bQflg8bNa45LKfn47WJN34rp/TwgEWvvc6r06dhpCl3ZbnXE2OSnMP30008wMTGpd15JSUmTAwgNDcW0adPQu3dv+Pv7Y82aNSgrK+M6a02dOhXOzs5Yvnw5AGDp0qV46qmn0L59exQWFmLlypVITU3FzJkzAdR03Jo7dy4+/fRTeHt7w9PTEwsWLICTkxNXvGizfVfuAwCeamdFRQXReq2ZD/TxRMPq8Hh8F5kEAAjsZAcXS0OVfyYhmkL5oPXdL6zAW9svAwCCOtvjtQGeGo6IkMdrUmHh5uaGH3/88YnLNMWkSZOQk5ODhQsXIjMzEz169MCRI0e4eyDT0tLA5//Xp6CgoACzZs1CZmYmLC0t4efnh7Nnz6Jz587cMvPmzUNZWRlef/11FBYWYsCAAThy5Eid8ay1TXphBX6OqnkmR1Dnxp2RIURTVJEP9O1Ew7FbNWdb3xrihXcDvWkUF6K3KB+oxqZ/kpFdUgUvW2OsfN6XcgjRek0qLFJSUlQSREhISINnICIjI5Vef/311/j6668fuz4ej4elS5di6dKlrRWiWmw5k4zSqmpYG4vwtK+TpsMh5LFUkQ/06UQDYwx382s6yk3u4waxgUCln0eIJlE+UI2YuwUAgLeGtKeH4RGd0KThhU6cOIHOnTvX2/mjqKgIXbp0wT///NNqwbUlmUWV2HE+DQAwb6QPbE3r3tNJiDZRVT4ICQlBamoqqqqqcP78eQQEBHDzIiMjsXXrVu71119/zS2bmZmJgwcPomfPnkrrqz3RkJmZicrKShw/fhwdOnRoclxNFZ9ViqpqBYQCHhwttPNLCyGthfJB60vLK8eVu4UAAD93S80GQ0gjNamwWLNmDWbNmlXvyCjm5uZ44403sHr16lYLri0Jv5WJMqkclkZCulpBdALlg8f75VwKAKB/exsI9WSIaEIaQvmg9d3OLAZjgLedCTxs6h9JkhBt06S/dlevXsXIkSMbnB8UFITo6OgWB6WP+Hw+evfujd69eytduq0VnVpzuXNiH1cYiZrcp54QtaN80DDGGE7F5wAAXgpw13A0hKge5YPWd/BaBgDA277+DvGEaKMmP8ficUNVGRgYICcnp8VB6SNDQ0NcvHix3nlxmSU4fjsbANDH3UqdYRHSbJQPGrb9fBru5ldAJOAjoB0d00T/UT5oXZUyOcIfDP7w+iAvDUdDSOM16YqFs7Mzbty40eD8a9euKY0NTZ4so6gCz353BqVV1ejqbIZhHe00HRIhjUL5oH6lVdX47mTNuPlzhraHmZ4+2JKQh1E+aF23M4pRIZPDUCiAr4u5psMhpNGaVFiMHj0aCxYsQGVlZZ15FRUVWLRoEcaOHdtqwbUFZxPzUC6Vw8pYhM3T+oCvZU/TJKQhlA/qd+haBtKLKuFkLsGrAzw0HQ4hakH5oHXVXq0Y4mNLQ8wSndKkW6E++eQT7N27Fx06dEBISAh8fHwAALGxsVi/fj3kcjk+/vhjlQSq68rLy7kh727dugUjo5qn78Zn1Tw06BlfJ3ogHtEplA/qKquqxq/na55FM66nM0zpagVpIygftK4b6TWja/X2oFspiW5pUmFhb2+Ps2fPYvbs2QgLCwNjDEDN8G3BwcFYv349N740UcYYQ2pqKvf/WpnFNWd3HMypqCC6hfJBXcsP38a1e0UwFRvgRf+mPQyMEF1G+aD15JRU4UxiLgBgcAdbDUdDSNM0efghd3d3HDp0CAUFBUhMTARjDN7e3rC0pDGWm6N2NKh2NJQc0UGUD/4Tc7eQexbNVxN94WplpOGICFEvygetIzq1AHIFg4+9Kdrb0YhQRLc0e1xTS0tL9OnTpzVjaXMYYyiukAEA3KzpSwjRXZQPgL2X70HBgKDO9hjRmc7MkraL8kHLpBdWAAA8bOh7AdE99NQmDbp2rwjFldUw4PPgakkJhBBdFvPgCbnjezpTZ0tCSLOcSczFtqgUAICjuaFmgyGkGaiw0KBvTyQAAHp7WMJYTA/FI0RXJWaX4vr9IgCAly3dukAIaZ4FB24gNa8cADCqq4OGoyGk6ejbrAbFZ5UCAGYPaa/hSAghLbHlTDIYA3q5WaADPSWXENIMx25m4k5OGQAg4n+D6SQF0UlUWKgJj8fjhpvl8XhgjHEjQlHHbUJ0219X0wEAM/p70m1QhJBm+eJwLABgej8PKiqIzqLCQk2MjIxw8+ZN7nVBmRTSagUAwM5MrKmwCCEtVCGVo7iyGgAwyJuGhiSENF1eaRXu5NZcrXh7GN3FQHQX9bHQkITsmtugbEzEEBsINBwNIaS5knJqjmVLIyHMjeiBeISQpvv7WgYAoLuLOaxN6GQj0V1UWGjIn1fvAwB6uJprOBJCSEtkl9Tc0uhsSSO4EEKa59itTADAM75OGo6EkJahwkJNysvL0aVLF3Tp0gXl5eUoeXDrhL+nlYYjI4S0RF6pFABgZUxnGQkhTcMYw4GY+ziTmAeAnrRNdB/1sVATxhhu3brF/b+sqqawMJXQrROE6LL8sprCwtpYpOFICCG65puIRHx9PB4A4GplCG97Uw1HREjLUGGhIXkPvoxYGtGXEUJ0WW1hYUWFBSGkia7eKwQAjOhsj4VjO2s2GEJaAd0KpQGVMjliM0oAAE4WEg1HQwhpCSosCCHNEZ1agMi4bADAq/094WplpOGICGk5Kiw0oLhShgqZHADQzZk6bxOiy2TymmGjxQaUTgkhjfdzVAoUDBjXwwl9vaw1HQ4hrUIr/hKuX78eHh4ekEgkCAgIwIULFxpc9scff8TAgQNhaWkJS0tLBAYG1ll++vTp4PF4Sj8jR45U9WY0WkZhBYCa4SnpYVqEKNO1fMBabU2EkEfpWj5orLv55Th6s2YkqPE9nNX++YSoisYLi127diE0NBSLFi3C5cuX4evri+DgYGRnZ9e7fGRkJKZMmYKTJ08iKioKrq6uCAoKwv3795WWGzlyJDIyMrif3377TR2b0yh3C2oKCx8H6qRFyMN0MR+wB5UFnSQgpHXpYj5orF/Pp6JSpoC3nQkGeNuo/fMJURWNFxarV6/GrFmzMGPGDHTu3BkbN26EkZERNm/eXO/y27dvx1tvvYUePXqgY8eO+Omnn6BQKBAREaG0nFgshoODA/djaWmpjs1pEI/Hg7u7O9zd3ZH7YHhKO1PqX0HIw3QxHygeVBZ8qisIaVW6mA8aQ6Fg2HnhLgAgZFh7CAUa/ypGSKvR6KhQUqkU0dHRCAsL46bx+XwEBgYiKiqqUesoLy+HTCaDlZXy8yAiIyNhZ2cHS0tLDBs2DJ9++imsreu/h7GqqgpVVVXc6+LiYgCATCaDTCbjptf+/+FpjSUUCpGQkAAA+OJIHADAxljYrHXpi5a0J6lL19tR1/JBLYVCwf2ra78DOgaV6WN76Oq26GI+aOz+E5tZgqIKGYxEAgR1tNHZ39HD9PHYaQxd2251xKnRwiI3NxdyuRz29vZK0+3t7REbG9uodXz44YdwcnJCYGAgN23kyJGYMGECPD09kZSUhI8++gijRo1CVFQUBAJBnXUsX74cS5YsqTP92LFjMDKqO0pDeHh4o2JrSEwCHwAfeffv4NChpBatSx+0tD2JftDVfJCeUXM837p5E4fybzQqTm1Dx6AyfWqP8vJyTYfQLLqaD4An7z/ns3kABHCSyHDs6JFGbYuu0Kdjpyl0ZbvVkQ90+jkWX3zxBXbu3InIyEhIJP/dVjR58mTu/926dUP37t3h5eWFyMhIDB8+vM56wsLCEBoayr0uLi7m7s00MzPjpstkMoSHh2PEiBEQCpv/YLsdmy8CuQUY1KcHRvs6Nns9uq612pPUkMlkOHDggKbD0Bh154Nah4uvAnlZ6Nq1C0YHuLXyVqkWHYPK9LE9as+wtzWayAeN3X/O7L8J4D4GdvXE6FE+LdhK7aGPx05j6Np2qyMfaLSwsLGxgUAgQFZWltL0rKwsODg4PPa9q1atwhdffIHjx4+je/fuj122Xbt2sLGxQWJiYr2JQywWQywW15kuFArr3VEamv44FRUVGDRoEADA6LlPAQBOlsY6sSOqWnPak+gfXc0HtZ22DQwMdHY/pmNQmT61h65uh67mgyfNY4zh2v2aL3edncx19vfTEH06dppCV7ZbHTFqtMeQSCSCn5+fUseq2o5Wffv2bfB9K1aswLJly3DkyBH07t37iZ9z79495OXlwdFRc1cHFAoFLl26hEuXLiGnqBIAYGdWN1kR0lbpaj6o7bxNfbcJaT26mg+e5GZ6MWIzSyAy4KN/exoNiugfjQ9FEBoaih9//BHbtm3D7du3MXv2bJSVlWHGjBkAgKlTpyp13vryyy+xYMECbN68GR4eHsjMzERmZiZKS0sBAKWlpfjggw9w7tw5pKSkICIiAuPGjUP79u0RHByskW18VGlVNQDA3oxGhSLkYbqYD/4bbrZVVkcIeUAX88GTpOXX3OPe3dkcThaGavlMQtRJ430sJk2ahJycHCxcuBCZmZno0aMHjhw5wnXYSktLA5//X/2zYcMGSKVSPP/880rrWbRoERYvXgyBQIBr165h27ZtKCwshJOTE4KCgrBs2bJ6L2dqipFIABOxxpu/1cnl8kaPOiCTyWBgYIDKykrI5XIVR6Y/RCKR0jGhT3QxH9Q+II9H1ywIaVW6mA+epKSy5u+jmaH23zZDSHNoxTfbkJAQhISE1DsvMjJS6XVKSspj12VoaIijR4+2UmSqY2eqPUVOa2CMITMzE4WFhU16j4ODA+7evUsPF2sCPp8PT09PiEQiTYeiErqWD+iKBSGqo2v54Enu5JQBACyMqLAg+kkrCou2yNJYv74U1hYVdnZ2MDIyalShoFAoUFpaChMTE709A9/aFAoF0tPTkZGRATc3NyrItAI9II8Q8mSMMfxxueYp4EGdH98BnRBdRYWFhpjr0WVQuVzOFRUNPWSoPgqFAlKpFBKJhAqLJrC1tUV6ejqqq6t1YhQKfaeovWJBt0IRQh6juKIauaU1D9sb3MFWw9EQohpUWKiRjY0NKmU1T+nVp8Kitk9FQw8LIq2r9hYouVxOhYUWYNy9UJqNgxCi3a7fLwIA2JiIYCiq+zA+QvQBnSZWE2NjY+Tk5GDRrrPgiyR6VVjUotty1IPaWbv813mbEEIatuNCKgAgqAvdBkX0FxUWalZUUXN230IPCwtC2qLaCxZ8KvgIIY+RmF0z7G1gJzsNR0KI6lBhoWYF5VIAgLmRfnXeJqSt4h6QR3UFIaQBt9KLEZ9VU1h0cjTTcDSEqA4VFmpSUVGBIUOGYPeSmVDIqmCtZ6NC6aohQ4Zg7ty5mg6Do23xkMajwoIQ0pCNp5IAAMM62sHRnB6MR/QXdd5WE4VCgVOnTgEAXBnTu+Fm2zKpVKq3z5QgT8ZoVChCyGPEZ5Xgz6vpAICXAtw0HA0hqkVXLDTE2YLOWGja9OnTcerUKaxduxY8Hg88Hg9JSUl47bXX4OnpCUNDQ/j4+GDt2rV13jd+/Hh89tlncHJygo+PDwDg7Nmz6NGjByQSCXr37o39+/eDx+MhJiaGe++NGzcwatQomJiYwN7eHq+88gpyc3MbjOdJD3wimsdAt0IRQhr20z93AAC+LuYY6E3DzBL9RlcsNMDVyhDt7Uw0HYbKlZWVNThPIBAoneV/3LJ8Ph+GhoZPXNbY2LhJ8a1duxbx8fHo2rUrli5dCgCwtLSEi4sL9uzZA2tra5w9exavv/46HB0dMXHiRO69ERERMDMzQ3h4OACguLgYTz/9NEaPHo0dO3YgNTW1zi1NhYWFGDZsGGbOnImvv/4aFRUV+PDDDzFx4kScOHGi3nhsbemPkLb778nbVFkQQpRVVctx+HomAOCj0Z0gMqDzuUS/UWGhAR0d2kbHLROThoun0aNH46+//uJe29nZoby8vN5lBw8ejMjISO61h4cHd5b/YdzzBBrJ3NwcIpEIRkZGcHD4b/i/JUuWcP/39PREVFQUdu/erVRYGBsb46effuKKo40bN4LH4+HHH3+ERCJB586dcf/+fcyaNYt7z7p169CzZ098/vnn3LTNmzfD1dUV8fHx6NChQ73xEO3Gdd7WcByEEO0Tl1mCkqpqCPg89PGw0nQ4hKgcFRYa4GXXtDPrRL3Wr1+PzZs3Iy0tDRUVFZBKpejRo4fSMt26dVO64hIXF4fu3btDIpFw0/z9/ZXec/XqVZw8ebLegispKQkdOnRo3Q0havHfFQvNxkEI0S5388vx4o/nAQDWxiLw+ZQkiP6jwkIDTMVto9lLS0sbnCcQKD91NDs7u8Fl+XzlS8eq7Hewc+dOvP/++/jqq6/Qt29fmJqaYuXKlTh//rzSck297QqoaY+nn34aX375ZZ15jo6OzY6ZaNZ/D8ijLw2EkBrSagWWH76N0qpqdHQwxTdTemo6JELUom18w9USApEECsbQzrZtXLF40pdvhULR6GWbst6mEIlEkMvl3OszZ86gX79+eOutt7hpSUlJT1yPj48Pfv31V1RVVUEsFgMALl68qLRMr1698Mcff8DDwwMGBvUfeo/GQ3QA94A8zYZBCNEeP/5zB4ce9K14P8gHHexNNRwRIepBvYjUxNjYGIM+Pwy30D9gbWGu6XDIAx4eHjh//jxSUlKQm5sLb29vXLp0CUePHkV8fDwWLFhQp0Coz4svvgiFQoHXX38dt2/fxtGjR7Fq1SoA/3XqnTNnDvLz8zFlyhRcvHgRSUlJOHr0KGbMmMEVE4/G83DxRbQTPSCPEPKoK2kFAICx3R0R2Nlew9EQoj5UWKhJVbUcd/NrOic7mEmesDRRl/fffx8CgQCdO3eGra0tgoODMWHCBEyaNAkBAQHIy8tTunrREDMzM/z111+IiYlBjx498PHHH2PhwoUAwPW7cHJywpkzZyCXyxEUFIRu3bph7ty5sLCw4G73ejSetLQ01W08aRX/DRlAlQUhBCgsl+FUfA4AIGRYew1HQ4h60a1QapKSWw6ZnMFUbAB3ayNNh0Me6NChA6KiopSmbdmyBVu2bFGatnz5cu7/W7durXdd/fr1w9WrV7nX27dvh1AohJvbfw9E8vb2xt69e5sUD9FujK5YEEIecjw2GzI5QydHszYzCiQhtaiwUJPb93KRvWcxKgyFqAobrDR6ENEPP//8M9q1awdnZ2dcvXqVe0bFw8/gIPpHwfWxoMqCEALklUoBAF2dqKggbQ8VFmpyJ7sEFXcuoQKgzrl6KjMzEwsXLkRmZiYcHR3xwgsv4LPPPtN0WETF/hsVihBCgDJpNQA62UDaJios1OR+Yf0PfyP6Y968eZg3b56mwyDqRrdCEUIeEptZAgDwtm/4IbGE6CvqvK0mCVkNP9OBEKK76FYoQkitagUQnVoIAOhMt0KRNkgrCov169fDw8MDEokEAQEBuHDhwmOX37NnDzp27AiJRIJu3brh0KFDSvMZY1i4cCEcHR1haGiIwMBAJCQkqHITGlRaVY1VR+MQc7dII59PCFEtVnszFNUVhLR5OZVAcWU1xAZ8BHhaazocQtRO44XFrl27EBoaikWLFuHy5cvw9fVFcHBwg09iPnv2LKZMmYLXXnsNV65cwfjx4zF+/HjcuHGDW2bFihX45ptvsHHjRpw/fx7GxsYIDg5GZWWlyrajWq5AQZkUaXnluHG/CGeTcrHwwA10X3wU604mquxztQk9c0E9akch0le6dqKhdrenuoKQ1qdr+eBeWU0m8HEwhYCemknaII33sVi9ejVmzZqFGTNmAAA2btyIgwcPYvPmzZg/f36d5deuXYuRI0figw8+AAAsW7YM4eHhWLduHTZu3AjGGNasWYNPPvkE48aNA1AzWo+9vT3279+PyZMntyjeimrgtZ+jUVolR3FlNUoqZSiprEa5tOEO2SIBHzOGtMNHX7foo7WWSCQCn89Heno6bG1tIRKJuIfCPY5CoYBUKkVlZSX3HAfyeIwx5OTkgMfjQSgUajqcVld7omHjxo0ICAjAmjVrEBwcjLi4ONjZ2dVZvvZEw/LlyzF27Fjs2LED48ePx+XLl9G1a1cA/51o2LZtGzw9PbFgwQIEBwfj1q1brTI6W22ZR7dCEdK6dDEfpNcWFvSkbdJGabSwkEqliI6ORlhYGDeNz+cjMDCwwbH8o6KiEBoaqjQtODgY+/fvBwAkJycjMzMTgYGB3Hxzc3MEBAQgKiqq3sKiqqoKVVVV3Ovi4mIAgEwmg0wm46bLZDIIeMDphLwGt8lIJICp2AAmEgOYSgzgaCbB4qc7QQwZPnpoPQ+vVx+4uroiKysL9+/fb/R7GGOorKyERCJpVCFCavB4PDg6OkKhUChdJdKHfUrXTjQA9BwLQlRFF/NB1YOU7GxJw4yTtkmjhUVubi7kcjns7ZUfd29vb4/Y2Nh635OZmVnv8pmZmdz82mkNLfOo5cuXY8mSJXWmHzt2DEZGyg+zE/KBl7zkkBgAhgJAImAw5P4PCPjVAKqU3nPuVM2X7dri59SpU/XGoQ/4fD5dfVAxuVyO+Ph4TYfR6nTtREMtkYAHiZAPKBQ6V9zVxqtrcauKPraHrm6LLuaD2pOPhkI+BGA62/bNoY/HTmPo2narI06N3wqlDcLCwpSSUXFxMVxdXREUFAQzs/9GdZDJZAgPD8fHLwXq5W0o6lbbniNGjKD2bAUymQwHDhzQdBjNposnGgDgNTcAbkB+7Dkcqj9MrRceHq7pELSKPrVHebluDnWuq/ngOU/gOU8pUBaLQ7qaEFpAn46dptCV7VZHPtBoYWFjYwOBQICsrCyl6VlZWXBwcKj3PQ4ODo9dvvbfrKwsODo6Ki3To0ePetcpFoshFovrTBcKhfV+4W1oOmkeak+iTRp7okEfUHGvTB/bo/YMO2mepuQDfdx/Gqutbruubbc68oFGCwuRSAQ/Pz9ERERg/PjxAGo69EZERCAkJKTe9/Tt2xcRERGYO3cuNy08PBx9+/YFAHh6esLBwQERERFcIVFcXIzz589j9uzZqtwcQkgL6OqJBn2gz9vWHPrUHrq6HbqcD/Rp/2mqtrrturLd6ohR4zfDh4aG4scff8S2bdtw+/ZtzJ49G2VlZVxnralTpyrdY/nuu+/iyJEj+OqrrxAbG4vFixfj0qVLXCHC4/Ewd+5cfPrpp/jzzz9x/fp1TJ06FU5OTlzxQgjRPg+faKhVe6Kh9sTBo2pPNDysoRMNtWpPNDS0TkKI5lE+IEQ3abyPxaRJk5CTk4OFCxciMzMTPXr0wJEjR7h7INPS0pQ6A/fr1w87duzAJ598go8++gje3t7Yv38/N5QcAMybNw9lZWV4/fXXUVhYiAEDBuDIkSONHkqudpSXRy8ZyWQylJeXo7i4WCcqU21H7dm6atsT0N1nXYSGhmLatGno3bs3/P39sWbNmjonGpydnbF8+XIANScaBg8ejK+++gpjxozBzp07cenSJfzwww8AlE80eHt7c8NLNuVEQ0P5QB/QMahMH9ujdr/VxZyga/lAH/efxmqr265r262WfMBIHXfv3mWoGZ6efuhHJ3/u3r2r6cOo2b799lvm5ubGRCIR8/f3Z+fOnePmDR48mE2bNk1p+d27d7MOHTowkUjEunTpwg4ePKg0X6FQsAULFjB7e3smFovZ8OHDWVxcXKPjoXxAP/rwo6s5gfIB/dBP6/+oMh/wGNPB0xgqplAokJ6eDlNTU6XnK9R22rp7967edeLUBGrP1lXbnrdu3YKPjw8N+9tKGsoH+oCOQWX62B6MMZSUlMDJyYlyQit4XD7Qx/2nsdrqtuvadqsjH2j8VihtxOfz4eLi0uB8MzMzndiBdAW1Z+tydnamLxCt6En5QB/QMahM39rD3Nxc0yHojcbkA33bf5qirW67Lm23qvMBffsghBBCCCGEtBgVFoQQQgghhJAWo8KiCcRiMRYtWlTvmNak6ag9Wxe1J2kq2meUUXuQlmjL+09b3fa2ut2PQ523CSGEEEIIIS1GVywIIYQQQgghLUaFBSGEEEIIIaTFqLAghBBCCCGEtBgVFoQQQgghhJAWo8KCEEIIIYQQ0mJUWDxk/fr18PDwgEQiQUBAAC5cuPDY5ffs2YOOHTtCIpGgW7duOHTokJoi1R1NadOtW7eCx+Mp/UgkEjVGq91Onz6Np59+Gk5OTuDxeNi/f/8T3xMZGYlevXpBLBajffv22Lp1q8rjJNrnSfsOYwwLFy6Eo6MjDA0NERgYiISEBM0Eq2LLly9Hnz59YGpqCjs7O4wfPx5xcXFKy1RWVmLOnDmwtraGiYkJnnvuOWRlZWkoYqIrmvodQtfQsVPjiy++AI/Hw9y5c7lpbWG7G4sKiwd27dqF0NBQLFq0CJcvX4avry+Cg4ORnZ1d7/Jnz57FlClT8Nprr+HKlSsYP348xo8fjxs3bqg5cu3V1DYFADMzM2RkZHA/qampaoxYu5WVlcHX1xfr169v1PLJyckYM2YMhg4dipiYGMydOxczZ87E0aNHVRwp0TZP2ndWrFiBb775Bhs3bsT58+dhbGyM4OBgVFZWqjlS1Tt16hTmzJmDc+fOITw8HDKZDEFBQSgrK+OWee+99/DXX39hz549OHXqFNLT0zFhwgQNRk20XXP+3ukaOnaAixcv4vvvv0f37t2Vpuv7djcJI4wxxvz9/dmcOXO413K5nDk5ObHly5fXu/zEiRPZmDFjlKYFBASwN954Q6Vx6pKmtumWLVuYubm5mqLTbQDYvn37HrvMvHnzWJcuXZSmTZo0iQUHB6swMqLtHt13FAoFc3BwYCtXruSmFRYWMrFYzH777TcNRKhe2dnZDAA7deoUY6xm24VCIduzZw+3zO3btxkAFhUVpakwiZZr6t87fdDWjp2SkhLm7e3NwsPD2eDBg9m7777LGNP/7W4qumIBQCqVIjo6GoGBgdw0Pp+PwMBAREVF1fueqKgopeUBIDg4uMHl25rmtCkAlJaWwt3dHa6urhg3bhxu3rypjnD1Eu2jpDGSk5ORmZmptK+Ym5sjICCgTewrRUVFAAArKysAQHR0NGQymVJ7dOzYEW5ubm2iPUjTNffvna5ra8fOnDlzMGbMmDp/V/V9u5uKCgsAubm5kMvlsLe3V5pub2+PzMzMet+TmZnZpOXbmua0qY+PDzZv3owDBw7g119/hUKhQL9+/XDv3j11hKx3GtpHi4uLUVFRoaGoiLapPR7bYj5TKBSYO3cu+vfvj65duwKoaQ+RSAQLCwulZdtCe5Dmac7fO13X1o6dnTt34vLly1i+fHmdefq83c1hoOkACKnVt29f9O3bl3vdr18/dOrUCd9//z2WLVumwcgIIfpozpw5uHHjBv79919Nh0KITmlLx87du3fx7rvvIjw8nAaUaQS6YgHAxsYGAoGgTg/+rKwsODg41PseBweHJi3f1jSnTR8lFArRs2dPJCYmqiJEvdfQPmpmZgZDQ0MNRUW0Te3x2NbyWUhICP7++2+cPHkSLi4u3HQHBwdIpVIUFhYqLa/v7UGarzX+3umStnbsREdHIzs7G7169YKBgQEMDAxw6tQpfPPNNzAwMIC9vb1ebndzUWEBQCQSwc/PDxEREdw0hUKBiIgIpTPoD+vbt6/S8gAQHh7e4PJtTXPa9FFyuRzXr1+Ho6OjqsLUa7SPksbw9PSEg4OD0r5SXFyM8+fP6+W+whhDSEgI9u3bhxMnTsDT01Npvp+fH4RCoVJ7xMXFIS0tTS/bg7Rca/y90wVt9dgZPnw4rl+/jpiYGO6nd+/eeOmll7j/6+N2N5ume49ri507dzKxWMy2bt3Kbt26xV5//XVmYWHBMjMzGWOMvfLKK2z+/Pnc8mfOnGEGBgZs1apV7Pbt22zRokVMKBSy69eva2oTtE5T23TJkiXs6NGjLCkpiUVHR7PJkycziUTCbt68qalN0ColJSXsypUr7MqVKwwAW716Nbty5QpLTU1ljDE2f/589sorr3DL37lzhxkZGbEPPviA3b59m61fv54JBAJ25MgRTW0C0ZAn7TtffPEFs7CwYAcOHGDXrl1j48aNY56enqyiokLDkbe+2bNnM3NzcxYZGckyMjK4n/Lycm6ZN998k7m5ubETJ06wS5cusb59+7K+fftqMGqi7Z70904f0LHzn4dHhWKs7Wx3Y1Bh8ZBvv/2Wubm5MZFIxPz9/dm5c+e4eYMHD2bTpk1TWn737t2sQ4cOTCQSsS5durCDBw+qOWLt15Q2nTt3Lresvb09Gz16NLt8+bIGotZOJ0+eZADq/NS24bRp09jgwYPrvKdHjx5MJBKxdu3asS1btqg9bqJ5T9p3FAoFW7BgAbO3t2disZgNHz6cxcXFaTZoFamvHQAoHRsVFRXsrbfeYpaWlszIyIg9++yzLCMjQ3NBE53wuL93+oCOnf88Wli0le1uDB5jjKnv+gghhBBCCCFEH1EfC0IIIYQQQkiLUWFBCCGEEEIIaTEqLAghhBBCCCEtRoUFIYQQQgghpMWosCCEEEIIIYS0GBUWhBBCCCGEkBajwoIQQgghhBDSYlRYEEIIIYQQQlqMCgvyWDwe77E/ixcvRkpKitI0kUiE9u3b49NPP8XDz19cvHgxevTo0eBnDRkypN7PePPNN+uNx8zMDH369MGBAwdU2QSEkEaaPn16nWO21pw5c8Dj8TB9+nRuWmZmJt5++220a9cOYrEYrq6uePrppxEREcEtc/XqVTzzzDOws7ODRCKBh4cHJk2ahOzsbHVsEiE6Y/r06Rg/frxGY6j9PhATE9Oi9QwZMgRz585tcP6Tvk8A2tEej9LGmFobFRbksTIyMrifNWvWwMzMTGna+++/zy17/PhxZGRkICEhAUuWLMFnn32GzZs3N+nzZs2apbT+jIwMrFixQmmZLVu2ICMjA5cuXUL//v3x/PPP4/r1662yvYSQlnF1dcXOnTtRUVHBTausrMSOHTvg5ubGTUtJSYGfnx9OnDiBlStX4vr16zhy5AiGDh2KOXPmAABycnIwfPhwWFlZ4ejRo7h9+za2bNkCJycnlJWVqX3bCFGFnJwciEQilJWVQSaTwdjYGGlpaZoOS+etXbsWW7du5V4/qVhpihkzZuCTTz5plXU9TmRkJHg8HgoLC1X+Wa3FQNMBEO3m4ODA/d/c3Bw8Hk9pGgDk5uYCAKytrbl57u7u2LJlCy5fvozXXnut0Z9nZGRUZ/2PsrCwgIODAxwcHLBs2TKsXbsWJ0+eRLdu3Rr9OYQQ1ejVqxeSkpKwd+9evPTSSwCAvXv3ws3NDZ6entxyb731Fng8Hi5cuABjY2NuepcuXfDqq68CAM6cOYOioiL89NNPMDCo+XPl6emJoUOHqnGLCFGtqKgo+Pr6wtjYGOfPn4eVlZVSEU6ax9zcXCXrlcvl+Pvvv3Hw4EGVrF/X0RULohKXLl1CdHQ0AgICVPYZ1dXV2LRpEwBAJBKp7HMIIU3z6quvYsuWLdzrzZs3Y8aMGdzr/Px8HDlyBHPmzFEqKmpZWFgAqDmxUV1djX379indVkmIPjl79iz69+8PAPj333+5/z+OXC5HaGgoLCwsYG1tjXnz5tU5RhQKBZYvXw5PT08YGhrC19cXv//+u9IyN2/exNixY2FmZgZTU1MMHDgQSUlJ3PuXLl0KFxcXiMVi9OjRA0eOHFF6/4ULF9CzZ09IJBL07t0bV65cqRPrjRs3MGrUKJiYmMDe3h6vvPIKd0ISAMrKyjB16lSYmJjA0dERX331VeMaDsD3338PV1dXGBkZYeLEiSgqKuLmPXzb0fTp03Hq1CmsXbuWu506JSUFBQUFeOmll2BrawtDQ0N4e3sr5a76nD17FkKhEH369Kl3/u+//45u3brB0NAQ1tbWCAwMbPAKa1VVFd555x3uVs8BAwbg4sWLAGqu6taeRLG0tKxzK6nWYoQ00pYtW5i5uXmd6cnJyQwAMzQ0ZMbGxkwoFDIA7PXXX1dabtGiRczX17fB9Q8ePJgJhUJmbGys9PPrr79yywBgEomEGRsbMz6fzwAwDw8PlpeX11qbSQhppmnTprFx48ax7OxsJhaLWUpKCktJSWESiYTl5OSwcePGsWnTprHz588zAGzv3r1PXOdHH33EDAwMmJWVFRs5ciRbsWIFy8zMVMPWEKI6qampzNzcnJmbmzOhUMgkEgkzNzdnIpGIicViZm5uzmbPnt3g+7/88ktmaWnJ/vjjD3br1i322muvMVNTUzZu3DhumU8//ZR17NiRHTlyhCUlJbEtW7YwsVjMIiMjGWOM3bt3j1lZWbEJEyawixcvsri4OLZ582YWGxvLGGNs9erVzMzMjP32228sNjaWzZs3jwmFQhYfH88YY6ykpITZ2tqyF198kd24cYP99ddfrF27dgwAu3LlCmOMsYKCAmZra8vCwsLY7du32eXLl9mIESPY0KFDuThnz57N3Nzc2PHjx9m1a9fY2LFjmampKXv33Xcb3P5FixYxY2NjNmzYMHblyhV26tQp1r59e/biiy9yy9TmI8YYKywsZH379mWzZs1iGRkZLCMjg1VXV7M5c+awHj16sIsXL7Lk5GQWHh7O/vzzz8f+7t5///06329qpaenMwMDA7Z69WqWnJzMrl27xtavX89KSkrqxMQYY++88w5zcnJihw4dYjdv3mTTpk1jlpaWLC8vj1VXV7M//viDAWBxcXEsIyODFRYWPjY2bUCFBWm0JxUWf/75J0tISGC3bt1iu3fvZvb29uzDDz/klmtMYTF9+nSWkJCg9FNcXMwtA4Bt2LCBJSQksKNHj7LOnTuz48ePt+ZmEkKa6eE/mhMmTGCLFy9mixYtYs899xxjjHGFxblz5xpdWDDGWG5uLtu9ezf73//+x9q1a8csLCzYtWvXVLUZhKicTCZjycnJ7OrVq0woFLKrV6+yxMREZmJiwk6dOsWSk5NZTk5Og+93dHRkK1asUFqfi4sLd/xVVlYyIyMjdvbsWaX3vfbaa2zKlCmMMcbCwsKYp6cnk0ql9X6Gk5MT++yzz5Sm9enTh7311luMMca+//57Zm1tzSoqKrj5GzZsUCosli1bxoKCgpTWcffuXe7LcklJCROJRGz37t3c/Ly8PGZoaPjEwkIgELB79+5x0w4fPsz4fD7LyMhgjNX9Ej948OA663z66afZjBkzGvyc+nh7e7O///673nnR0dEMAEtJSal3/sMxlZaWMqFQyLZv387Nl0qlzMnJifvdnjx5kgFgBQUFTYpRk6iPBWk1rq6uaN++PQCgU6dOSEpKwoIFC7B48WJIJJJGrcPc3JxbR0McHBzQvn17tG/fHlu2bMHo0aNx69Yt2NnZtXgbCCGt49VXX0VISAgAYP369UrzvL29wePxEBsb26h1WVtb44UXXsALL7yAzz//HD179sSqVauwbdu2Vo+bEHUwMDCAh4cHdu/ejT59+qB79+44c+YM7O3tMWjQoMe+t6ioCBkZGUq3GhsYGKB3797c7VCJiYkoLy/HiBEjlN4rlUrRs2dPAEBMTAwGDhwIoVBY5zOKi4uRnp5e57as/v374+rVqwCA27dvo3v37kp/3/v27au0/NWrV3Hy5EmYmJjU+YykpCRUVFRAKpUqbYuVlRV8fHwe2wYA4ObmBmdnZ6XPVigUiIuLe2JfzVqzZ8/Gc889h8uXLyMoKAjjx49Hv379Glz+9u3bSE9Px/Dhw+ud7+vri+HDh6Nbt24IDg5GUFAQnn/+eVhaWtZZNikpCTKZTKmNhUIh/P39cfv27UbFr42osCAqIxAIUF1dDalU2ujCoqn8/f3h5+eHzz77DGvXrlXJZxBCmm7kyJGQSqXg8XgIDg5WmmdlZYXg4GCsX78e77zzTp1+FoWFhVw/i0eJRCJ4eXnRqFBEp3Xp0gWpqamQyWRQKBQwMTFBdXU1qqurYWJiAnd3d9y8ebPZ6y8tLQUAHDx4UOnLNwCIxWIAgKGhYfM3oAlxPP300/jyyy/rzHN0dERiYqLKY3icUaNGITU1FYcOHUJ4eDiGDx+OOXPmYNWqVfUu/+eff2LEiBENfqcRCAQIDw/H2bNncezYMXz77bf4+OOPcf78eaXBK/QZdd4mrSYvLw+ZmZm4d+8eDh8+jLVr12Lo0KEwMzPjlqmoqEBMTIzST21HMQAoLy9HZmam0k9BQcFjP3fu3Ln4/vvvcf/+fZVtGyGkaQQCAW7fvo1bt25BIBDUmb9+/XrI5XL4+/vjjz/+QEJCAm7fvo1vvvmGO+v5999/4+WXX8bff/+N+Ph4xMXFYdWqVTh06BDGjRun7k0ipNUcOnQIMTExcHBwwK+//oqYmBh07doVa9asQUxMDA4dOtTge83NzeHo6Ijz589z06qrqxEdHc297ty5M8RiMdLS0rgr/LU/rq6uAIDu3bvjn3/+gUwmq/MZZmZmcHJywpkzZ5SmnzlzBp07dwZQc2fCtWvXUFlZyc0/d+6c0vK9evXCzZs34eHhUScOY2NjeHl5QSgUKm1LQUEB4uPjn9iGaWlpSE9PV/psPp/f4NUOkUgEuVxeZ7qtrS2mTZuGX3/9FWvWrMEPP/zQ4GceOHDgibmHx+Ohf//+WLJkCa5cuQKRSIR9+/bVWc7LywsikUipjWUyGS5evMi1ce3ANPXFrbU0fS8W0R1P6mNR+yMQCJiLiwubNWsWy87O5pZbtGiR0nK1P8OHD2eM1dz/WN/84OBgbh0A2L59+5Q+X6FQsI4dOz62oxshRPUevaf5UbV9LGqlp6ezOXPmMHd3dyYSiZizszN75pln2MmTJxljjCUlJbFZs2axDh06MENDQ2ZhYcH69OnDtmzZotLtIEQdMjIymFgsZhUVFayyspJJJBKWnp7eqPd+8cUXzMrKiu3bt4/dvn2bzZo1q07n7Y8//phZW1uzrVu3ssTERBYdHc2++eYbtnXrVsZYTd8la2trrvN2fHw8+/nnn7nO219//TUzMzNjO3fuZLGxsezDDz+s03nbxsaGvfzyy+zmzZvs4MGDrH379kp9LO7fv89sbW3Z888/zy5cuMASExPZkSNH2PTp01l1dTVjjLE333yTubu7s4iICHb9+nX2zDPPMBMTk0Z13g4MDGQxMTHs9OnTrEOHDmzy5MncMo/mo1mzZrE+ffpw/VfkcjlbsGAB279/P0tISGA3btxgY8eOZf7+/vV+ZlZWFhMKhY/t+3Lu3Dn22WefsYsXL7LU1FS2e/duJhKJ2KFDh+qN6d1332VOTk7s8OHDSp238/PzGWM1Hex5PB7bunUry87O5jqBazMqLAghhBBC1Oy3335jAwYMYIwxdvr0ada+fftGv1cmk7F3332XmZmZMQsLCxYaGsqmTp2q9KVVoVCwNWvWMB8fHyYUCpmtrS0LDg5mp06d4pa5evUqCwoKYkZGRszU1JQNHDiQJSUlMcYYk8vlbPHixczZ2ZkJhULm6+vLDh8+rBRHVFQU8/X1ZSKRiPXo0YMbxai2sGCMsfj4ePbss88yCwsLZmhoyDp27Mjmzp3LFAoFY6ymQHn55ZeZkZERs7e3ZytWrKi3o/XDageD+e6775iTkxOTSCTs+eef576QM1b3S3xcXBx76qmnmKGhIQPAkpOT2bJly1inTp2YoaEhs7KyYuPGjWN37typ9zN/+ukn1r9//8f+Xm7dusWCg4OZra0tE4vFrEOHDuzbb79tMKaKigr29ttvMxsbGyYWi1n//v3ZhQsXlNa5dOlS5uDgwHg8ntKJGW3FY4wGByeEEEIIIaQhzzzzDAYMGIB58+ZpOhStRn0sCCGEEEIIeYwBAwZgypQpmg5D69EVC0IIIYQQQkiL0RULQgghhBBCSItRYUEIIYQQQghpMSosCCGEEEIIIS1GhQUhhBBCCCGkxaiwIIQQQgghhLQYFRaEEEIIIYSQFqPCghBCCCGEENJiVFgQQgghhBBCWowKC0IIIYQQQkiLUWFBCCGEEEIIaTEqLAghhBBCCCEtRoUFIYQQQgghpMWosCCEEEIIIYS0mIGmA9BGCoUC6enpMDU1BY/H03Q4hDQaYwwlJSVwcnICn0/nDVoD5QOiyygntC7KB0SXqSMfUGFRj/T0dLi6umo6DEKa7e7du3BxcdF0GHqB8gHRB5QTWgflA6IPVJkPqLCoh6mpKYCahjczM9NwNNpFJpPh2LFjCAoKglAo1HQ4OkUdbVdcXAxXV1duHyYtp6l8QMfa41H7PJlMJsP+/fsxc+ZMygmtpLYdf/rpJ4wfP572vVZGx7XqqC0fMA06deoUGzt2LHN0dGQA2L59+574npMnT7KePXsykUjEvLy82JYtW+oss27dOubu7s7EYjHz9/dn58+fb1JcRUVFDAArKipq0vvaAqlUyvbv38+kUqmmQ9E56mg7Xd53KR8oo2Pt8ah9nkwqlbIdO3ZQTnhES3JCbT7YsWMH7XsqQMe16qgrH2j0hsuysjL4+vpi/fr1jVo+OTkZY8aMwdChQxETE4O5c+di5syZOHr0KLfMrl27EBoaikWLFuHy5cvw9fVFcHAwsrOzVbUZhJBWQPmAEPIwygmE6CCVlSxNhEacjZg3bx7r0qWL0rRJkyax4OBg7rW/vz+bM2cO91oulzMnJye2fPnyRseiy2d9VY3OJjRfa7VdtVzBpNXyeufpy75L+YCOtSeh9qmRUVjBFApFvfN0/YrFw7QlJ9AVC9WSSqVs2246rlVBXflAp/pYREVFITAwUGlacHAw5s6dCwCQSqWIjo5GWFgYN5/P5yMwMBBRUVENrreqqgpVVVXc6+LiYgA196PJZLJW3ALdV9se1C5N19y2UygYrt4rwrnkfFxKLUB0WiGWj++CUV0dGvyMtkDf8wEda49H7QNE3M7GB3tvYPZgT8wa4FlnfltrG1XkhIbyAdD22lcdsovK8MVVAU6XXcYXE7rB3JD6WbQWde2vOlVYZGZmwt7eXmmavb09iouLUVFRgYKCAsjl8nqXiY2NbXC9y5cvx5IlS+pMP3bsGIyMjFoneD0THh6u6RB01uPaTsGAQimQVcFDVkXNv7cLeCiQKg9ruPd0DFiaos77y8vLWz1ebdVW8gEda4/XFtuHMSAqm4fdd/hg4OGPs/FwLLoNfhsf/VQVOaGhfAC0zX1P1XYm8VFezcfNtBycjgiHgEZI1jk6VVioSlhYGEJDQ7nXtSPrBAUF0ahQj5DJZAgPD8eIESNoxIYmerTtGGPIL5MiMacMCdmlOJ9cgPPJ+Sgor3tWwVgswAAva/TxsEQfD0v42JtCUM+3iIfPppHm0ZZ8QMfa47XV9jl6MwvrI+/gdmYJAGBCTyd8Oq4zhPV8A5PJZDhw4IC6Q9QrDeUDAG1u31M1hYLhg6URABRY/nxP9Pe203RIekVd+UCnCgsHBwdkZWUpTcvKyoKZmRkMDQ0hEAggEAjqXcbBoe5tI7XEYjHEYnGd6UKhkJJGA6htmocxILNUhq/CY3E2KbfeIkIo4MHD2hhetibwsjNGN2cLDPGxhUQoeOL629LvpK3kAzrWHq8ttc+ZxFyE7LwKADASCfDmYC+8NcQLBnRaF4BqckJD+QBoW/ueOuSXSSGV11yJ929nQ22ro3SqsOjbty8OHTqkNC08PBx9+/YFAIhEIvj5+SEiIgLjx48HUPOUzIiICISEhKg7XELAGMO2sym4kJKPlNwyJGULUHXuX24+jwe4WBqiva0JerlZol97a3R3saj37CNRRvmAtCXl0mq889sVAICNiRjHQwfBwkik4ai0C+UE3Vb9oKjggdHfQB2m0cKitLQUiYmJ3Ovk5GTExMTAysoKbm5uCAsLw/379/Hzzz8DAN58802sW7cO8+bNw6uvvooTJ05g9+7dOHjwILeO0NBQTJs2Db1794a/vz/WrFmDsrIyzJgxQ+3bR9q2M4m5+OH0HZyKz3loas3tS+3tTPB+UAcM7mAHQ9GTr0S0BZQPCKlfUbkMS/++hbwyKQDg4DsD2kRRQTmhbWGaDoC0Co0WFpcuXcLQoUO517X3MU6bNg1bt25FRkYG0tLSuPmenp44ePAg3nvvPaxduxYuLi746aefEBwczC0zadIk5OTkYOHChcjMzESPHj1w5MiROp21CFGVvNIqLDhwA4euZ3LTXK0M8fEoH6TeuISXxgXDxEiiwQi1E+UDQuqqqpZj6ubzuHqvCADw0eiOsDdrG/mDckLb1MbHINB5Gi0shgwZAsYarlG3bt1a73uuXLny2PWGhITQZU2iEQevZWDBgRvIL5PCgM/DxD6ueLq7EwI8rSCXV+PQHUDciL4SbRHlA0KUnYrPwcqjsbhxvxgWRkJ8PakHhnSw1XRYakM5oW15zK+a6BCd6mNBiLbKL5NiwYEbOHgtAwDQ0cEUq17wRVdnc24ZuVxT0RFCdE1CVgle3XoRcgWDRMjH2sk9MbgNFRWkDaNLFjqNCgtCWkiuYHhl03ncTC+GgM/DnCFeCBnmDZEBdT4jhDRdUYUMobuvQq5gGOhtgzWTesDapP6RiQghRJtQYUFIC72z8wpuptc8P2Lz9D50VpEQ0iyMMRyIScc3EQm4k1sGSyMhPhvfjYoK0iYw6r6tF6iwIKQFKqRyRNyuGRM9dEQHKioIIc328f4b2HG+pjOytbEIv7wWADdr9T/tnRBNojuhdBsVFoQ0A2MMv5xLxfen7qBSpoCTuQRvDfHSdFiEEB1VVCHjioq5gd6Y0d8T5ob0gDDSdlDnbf1AhQUhzbDvyn0sPHATAGAiNsCK533p6beEkGapkMoRtvcagJorFXMDO2g4IkIIaR4qLAhpgkqZHP8k5GLFkTgAwLgeTvjs2W4wEdOhRAhpns8P3cah65kQCnhY9EwXTYdDiEbQBQv9QN+GCGmk0/E5CN19FbmlVQAASyMh5o3sSEUFIaTZ7uSU4vfoewCA9S/2QlAXBw1HRIhmUR8L3UbfiAh5gqziSnx28Db+vJoOALAzFWNsdydM6+cOZwtDDUdHCNFV9wsr8Nq2S6iQydHJ0QyBnejpz6TtetzDEInuoMKCkMfILqnE6LX/IK9MCh4PmPqUO8JGd4KEnp5NCGkBmVyBKT+cQ1p+OWxNxfjlNX/w+XSulhA6CnQbFRaENOB0fA7e+CUaFTI5xAZ8/DG7n9KTtAkhpDkSskqwOjweafnlAIDdb/SFDT2rgrRxdMFCP1BhQchDKmVyRCXl4dD1DOy9ch9yBYOPvSmWP9eNigpCSIvll0kxYcNZlFRWAwD+N6IDPG2MNRwVIVqELlnoNCosCHngbn45hqyKhFzx32mTUV0d8PWkHnTrEyGkxcqqqvHl4ViUVFbDRGyAzdP7oI+HpabDIoSQVkOFBWnzUvPK8PrP0YjLKuGmvRTghvE9ndHHw0qDkRFC9MWh6xn48PdrKKmquVIxN9Ab/p6UXwh5FF2w0G1UWJA2izGG6NQChO29joTsUgBALzcLvNLXHc/2dNFwdIQQffFLVAoWPHigJgCseL47XvCjHEPIw6iPhX6gwoK0SUXlMszZcRn/JuYCAAyFAmx8xQ+DO9hqODJCiD4prarGwj9rigo3KyPseuMpOJrTMNWEEP1EhQVpk744chv/JuaCxwNe8HPBnKHt4W5NHSgJIa0rvbACjAFmEgOc+N9gGAj4mg6JEEJUhgoL0uZcu1eI3y7cBQAsfroLpvXz0GxAhBC9dTI2GwDgYmlERQUhj8FQcy8U9bHQbVRYkDYjp6QKh29kYOGDe50HetvgpQA3DUdFCNFXxZUyrA6PBwAEdrLTcDSEEKJ6VFgQvVcurcbJ2BzM/+O/EVkA4LleLnQGkRCiMquPxaOqWgFrYxHmDGuv6XAI0Wpc5226ZKHTqLAgeuvozUzsuXQP/yTkoKpaAQAQGfDx7nBvDPS2QXcXC80GSAjRW6VV1TgVnwMACBnWHmIDehYOIUT/UWFB9NI/CTl445do7rWLpSGCOjtg5kBPOFnQiCyEENXZ9G8yVh+LQ5lUDqGAh6E+dBsUIU9Co83qB624D2T9+vXw8PCARCJBQEAALly40OCyQ4YMAY/Hq/MzZswYbpnp06fXmT9y5Eh1bArRAidjs/Hh79cA1NzXfPjdgfhn3lAsfLozFRU6gPIB0XVrjsejTCqHrakY307pBQ8bGnGuuSgftD10J5Ru0/gVi127diE0NBQbN25EQEAA1qxZg+DgYMTFxcHOru5Znr1790IqlXKv8/Ly4OvrixdeeEFpuZEjR2LLli3ca7FYrLqNIFrhXkE5/ryajhVH4gAANiYiLHq6C1ytjDQcGWksygdE15VVVaOksqYvV/h7g2BhJNJwRLqL8kHbwugJeXpB44XF6tWrMWvWLMyYMQMAsHHjRhw8eBCbN2/G/Pnz6yxvZWWl9Hrnzp0wMjKqkzjEYjEcHBxUFzjRGkduZODr8ATEZZVw0yyMhDg9byiMRBrfxUkTUD4guu7Eg+Fl3ayMqKhoIcoHbRNdsdBtGv3WJZVKER0djbCwMG4an89HYGAgoqKiGrWOTZs2YfLkyTA2Vr7UHBkZCTs7O1haWmLYsGH49NNPYW1tXe86qqqqUFVVxb0uLi4GAMhkMshksqZull6rbQ9taJdLqQXYfv4u/r6eyU3r42GJcb6OGN3VAUIe04o4a6mj7bRpe5uK8oEybTrWtJG2tU9RhQxRd/Lx9fEEAMCYbvYaj03Tn98S2p4PAN1uX20kk1U/9H9q29amrjbVaGGRm5sLuVwOe3t7pen29vaIjY194vsvXLiAGzduYNOmTUrTR44ciQkTJsDT0xNJSUn46KOPMGrUKERFRUEgqDsyx/Lly7FkyZI6048dOwYjI7qNpj7h4eEa/fysCuCLqwIoWM25DUMBw4e+cliKc4DsHPxz4ppG43scVbZdeXm5ytatapQP6qfpY03babp97pYC+1IEuFMCsAfnWo0NGOxKEnDoUIJGY9Nl2p4PAM3ve/omqwKo/VpKbau7dPo+kU2bNqFbt27w9/dXmj558mTu/926dUP37t3h5eWFyMhIDB8+vM56wsLCEBoayr0uLi6Gq6srgoKCYGZmproN0EEymQzh4eEYMWIEhEKhxuJY9NctKNg9+NibYMnTneDrYq71z6RQR9s9fDatrdG3fKAtx5q20ob2ySutwkdr/kVZlRwA0M7GGEN9bDBrgAesTTR/375MJsOBAwc0HYZGqDofAKBjs5Ul5ZTh85gzAKhtVUFd+UCjhYWNjQ0EAgGysrKUpmdlZT3x/seysjLs3LkTS5cufeLntGvXDjY2NkhMTKw3cYjF4no7bwmFQtqxG6DptjkVnwcA+GhMZzzV3lZjcTSHKttOl/dXygf10/Sxpu000T7/JOTgZGwODl3PQFmVHCZiAxx9bxCcadS5VqPt+QCgY7O1GRjUfCXlgdpWl2n0FK9IJIKfnx8iIiK4aQqFAhEREejbt+9j37tnzx5UVVXh5ZdffuLn3Lt3D3l5eXB0dGxxzESzsksqMeWHc7hfWAGg5gwh0Q+UD4guOHIjA69suoDNZ5KRWVwJoYCHVS/4UlHRyigfEKKbNH7vSGhoKH788Uds27YNt2/fxuzZs1FWVsaNAjF16lSlzlu1Nm3ahPHjx9fpcFVaWooPPvgA586dQ0pKCiIiIjBu3Di0b98ewcHBatkmojqf7LuBqDt5MODz8PJTbnCxpD/m+oTyAdFmybllePu3KwAAW1MxvpnSE2c+HIaRXWmEIVWgfNDWPBhuloaF0mka72MxadIk5OTkYOHChcjMzESPHj1w5MgRrsNWWloa+Hzl+icuLg7//vsvjh07Vmd9AoEA165dw7Zt21BYWAgnJycEBQVh2bJlNFa1jiutqkZkfA4AYPvMAAS0q38UD6K7KB8QbXb4RgZkcgY7UzGO/28wzCR0q4YqUT4gRPdovLAAgJCQEISEhNQ7LzIyss40Hx+fBh+kYmhoiKNHj7ZmeERLrAmPh7RaASdzCfp4WD35DUQnUT4g2qRCKsfGU0k4dD0DCdmlAIAJvVyoqFATygdtB6MLFnpBKwoLQp5EoWDYH3MfALD4mS7g8yn1EEJUp6pajlvpxQjZcYXr08XnAQO8bfHm4HYajo4QQrQTFRZEJxy9mYncUikMhQIM8bHTdDiEED0Vc7cQC/bfQGxmMWTymlOoJmIDLB3XBcM62tHTtAlRkfqvMxFdQ4UF0Xo37hdh/t7rAIDp/T0gMtD4mAOEED2TXliBr47F44/L97hpFkZCdHEyQ+iIDvBzp9svCSHkSaiwIFotObcML/54DsWV1ejlZoH3AjtoOiRCiB5acSQW+2PSAQBO5hJ8M6Un/NwtwePRbZeEqAP1sdAPVFgQrcUYw8f7rqO4sho93Syw7VV/ulpBCGlVidmlmPf7VVxOKwQArJnUA0/7OkFA/bgIIaTJqLAgWuvPq+k4m5QHiZCPNZN6wJRGYSGEtJBcwZBXVoXs4irklFRh8V83kZpXDgCY2tcdz/g60eAQhGgAo+dY6AUqLIjW+utqBgDg9YHt4G5NT9gmhDRNpUyOzKJKXLlbgH1X0hGXWYzcUinkirrdRD8a3RGvD/LSQJSEEKI/qLAgWkmuYDh+OwsAENSFnmpLCHm82MxibD2TgvSiSmQVVSKzuBJFFbJ6l+XxAGtjMezNxLA3k8DHwRQz+nuqOWJCyMMaePwI0TFUWBCttOZ4PADAUCiAt72JhqMhhGiTarkCN+4X499MHqIPxuJObjn+Tcytd1lDoQBuVkYY1c0BQ3zs4GAmgY2JCAYC6q9FiDaiO6F0GxUWRCsdvF5zG9RrAzwhNhBoOBpCiKaVVMpw+EYmDl3PwMXkfJRJ5QAEQHKa0nIfje6ITo5msDeTwN5MAjOJAY3sRIgOoCsW+oEKC6J1jt3MxJ2cMgj4PEzr56HpcAghGlJaVY0b94tw7k4eNv2bjJLKam6eqcQAzmIp+nf1RAcHM7S3M0VHB1MYi+nPGiG6jE4D6DbKwESrpOaV4e3frgAAxvVwgq2pWMMREULUpVquwL2CCkTdycOv51JxK6NY6SxmOxtjTOjljGEd7dHOWoKjRw5j9EgfCIU0YhwhhGgDKiyIVvn+9B1UVStgKjbA+0E+mg6HEKIiJZUyXEotwIXkfNzOKEZKbhnuFVSg+pERm5zMJejmYo6RXR0wzteZGwpWJqu/YzYhRDdxw80SnUaFBdG4Cqkc28+n4tjNLFxIyQcAvBvoDScLQw1HRghpbQoFw+rweGw8lVSniAAAsQEf7WxN8FwvZzzTwwl2phINREkIIaQ5mlRYTJ06FevXr4epqSkA4OrVq+jcuTNdhiYt8sPpO/j6wShQAPBcLxe8/JS7BiMijUH5gDTHr+dTse5kIgDA3doI/h5W6OFmAU8bY3jaGMPeVEIPqNNBlA9ISzF6Pp5eaNJ4e9u3b0dFRQX3euDAgbh7926rB0XajoSsEvx59T4AoKODKf79cCi+mugLiZBGgtJ2lA9IU0XczsLCAzcB1Iz4duqDoVj5gi9eCnBHPy8bOJobUlGhoygfEEKAJl6xYI+MBfboa0KaYvfFu/hk/w1I5QqYSgzwwyu94WJppOmwSCNRPiCNVVwpw9ydMTgRmw0A8LYzwYcjO2o4KtKaKB+QVkPnFnQa9bEgaqdQMCw4cAPbz9eMPz+sox0+f7YbHMzpXmpC9ElKbhmW/HUT/yTkcv0pgjrb439BPhAZ0APqCCFE3zS5sLh16xYyMzMB1JyRiI2NRWlpqdIy3bt3b53oiF76/fI9bD+fBh4PCBnaHqEjOtADrHQU5QNSH8YYjt7MxMIDN5FdUgUAcLUyxJJnumBYR3sNR0dUhfIBaQnqY6EfmlxYDB8+XOkS59ixYwEAPB4PjDHweDzI5fLWi5DonZMPboeYM6Q9/kdDyuo0ygfkUYnZJVj85y38m5gLAPC0Mca3U3qii5MZnUDQc5QPCCFNKiySk5NVFQdpI25nFOPYrSwAQD8vaw1HQ1qC8gF5GGMMXx37bxhZkQEfbw72wuzBXjAU0WAM+o7yAWkpeo6FfmjSTa7u7u6N+mmq9evXw8PDAxKJBAEBAbhw4UKDy27duhU8Hk/pRyJRvjefMYaFCxfC0dERhoaGCAwMREJCQpPjIq1v7fEEyBUM7WyM4edhqelwSAtQPiC1knJK8c7OGKw7mYhqBUNgJzuEvzcIoSM6UFHRRlA+IIQAzey8nZCQgAMHDiAlJQU8Hg+enp4YP3482rVr1+R17dq1C6Ghodi4cSMCAgKwZs0aBAcHIy4uDnZ2dvW+x8zMDHFxcdzrRy+vr1ixAt988w22bdsGT09PLFiwAMHBwbh161adJEPUKzGn5n7b+aM6QmxAXzj0AeWDtutufjnm7opBdGoBN+3d4d54b0QHDUZFNInyAWku6mOhJ1gTff7558zAwIDx+Xzm4ODA7O3tGZ/PZ0KhkK1cubKpq2P+/v5szpw53Gu5XM6cnJzY8uXL611+y5YtzNzcvMH1KRQK5uDgoBRLYWEhE4vF7LfffmtUTEVFRQwAKyoqatxGtCFSqZTt37+fSaXSJr83t6SSuX/4N3P/8G+WW1Kpgui0W0varrHUve9SPlAddewvLSGXK9jEjWe5Y/rVLRfYmcQcplAo1PL52t4+2kAqlbIdO3aobf9tS/lgx44dtO+1spi0Aub+4d+sx4K/qG1VQF35oElXLE6ePIlPPvkECxYswLvvvgtLy5pbWfLz87FmzRrMnz8f/v7+GDRoUKPWJ5VKER0djbCwMG4an89HYGAgoqKiGnxfaWkp3N3doVAo0KtXL3z++efo0qULgJr7PDMzMxEYGMgtb25ujoCAAERFRWHy5Ml11ldVVYWqqirudXFxMQBAJpNBJpM1alvaitr2aE673Lxfc1bT2UICMzG/zbVtS9quqZ+hDpQPVEsd+0tzFVfI8PO5NJxPzgcAfD6+C17wcwYAVFdXqyUGbW4fbUH5QHX5AKB9r7XJHuQOHo/aVhXU1aZNKiw2btyImTNnYvHixUrTrayssHTpUmRmZmLDhg2NThy5ubmQy+Wwt1ceftDe3h6xsbH1vsfHxwebN29G9+7dUVRUhFWrVqFfv364efMmXFxcuKHu6ltn7bxHLV++HEuWLKkz/dixYzAyoge21Sc8PLxRy0Vl8XA5j4fMch6KZTUXOIXVFTh06JAqw9NqjW275igvL1fZuv/P3n3HNXH+cQD/JJCEvYWAIqCgOMGJaOtEQWvr6FA7HLXWOqqWtiqtW1usrda2P6uts8NVW0eHpSKKdSAqihNREAGVsJS9QvL8/ohciWyBXMb3/Xrlpbm7XL7Pw90399zd89yTKB9oRnNuL/WVXQKczRDibgHU9mUACHBRwjz9Mg4fvsxLbNpQP8Tw8gFA215TS84HKg5LqW51V4MaFufOncNPP/1U4/w33ngDEydObHRQtfH394e/vz/3vm/fvujQoQO+++47rFy58qnWGRISguDgYO59Xl4eXF1dMWzYMFhZWTU6Zn0il8sRHh6OoUOHQiQS1bpscZkC762KgLLSQA8u1iaYM8wLI7o6N3Ok2qchdfe0Kp9Na26UD5qXJraX2qTnlWBfzH1E3srClfu5ePJByk5WErzYrSXmDG4LI6Hm74rmu350gVwux6FDhzTyXYaWDwDQttfELt/Lxbpr0QCobpuDpvJBgxoW6enpcHd3r3G+h4dHja3+6jg4OMDIyAjp6elVvkcqldZrHSKRCN26dUNCQgIAcJ9LT0+Hs/N/B6/p6enw9fWtdh0SiQQSiaTaddOGXb361M2aI7e5RsWBmX3h5WQJCwk97L05tytNbq+UDzRDE9+bXVCKeFk+Uh4WIeVhEZIfFuFEfCYKSv+7relZLweM7OqM9lIrtG1hDksT7ciNlKe1g6Hlg4r107bXdIyM/hvQhepWdzVouNmSkhKIxeIa54tEIpSVldV7fWKxGD169EBERAQ3TalUIiIiQu2sQ20UCgWuXr3KJQkPDw9IpVK1debl5SE6Orre6ySN9/fVNGw+qRrXfM1LXdGttS01KvQM5QPdl5lfiu9OJMI/9Bhe3RKNhfuv4tvIRPx1JQ0FpeXo6GyF1WO7IPqjIfhpqh/G9WoNX1cbrWlUEO1B+YAQAjzFcLNbtmyBhYVFtfPy8/MbHEBwcDAmTZqEnj17onfv3li/fj0KCwsxZcoUAMDEiRPRsmVLhIaGAgBWrFiBPn36wNPTEzk5Ofj888+RnJyMt956C4BqaLl58+Zh1apV8PLy4oaTc3FxwejRoxscH2m4eFk+5u6JBQBM7uuOV3q68hsQaTaUD3QTYwxLDl3Hz9HJ3C1OUisTeDtborWdGVrbmcHDwRzPerWA2LhB55+IAaN8QBqj4m5LGm5WtzWoYdG6dWts3ry5zmUaYty4ccjMzMSSJUsgk8ng6+uLsLAwrnNVSkoKhML/ftgePXqEadOmQSaTwdbWFj169MCZM2fQsWNHbpn58+ejsLAQb7/9NnJycvDMM88gLCyMxqjWkEOx91GmUKKDsxUWj+xY9weITqJ8oJtKyxVY9WccfjqbDADo2soaY7q1xCR/dwh56CtB9APlA0IIAAgYe7JLHsnLy4O1tTVyc3Op8/YT5HI5Dh8+jBEjRtR4/2Pgl/8iPj0fn73YBeN6NeyHRJ/Vp+4ai7bdpsdXnTbH9lJarsC4784iNjUHAPDpmC541U8391FN7E+6Ti6X49dff8Wrr75KOaGJVOSDXbt24aWXXqJtrwnFJD/CixvPwF7CcHZRINVtE9NUPmjQNe5jx46hY8eO1Y48k5ubi06dOuHkyZNNFhzRPakPi7inaw9oV/2TUYl+oHygW/ZdSIV/6DGuUfHdGz10tlFBtA/lA0II0MCGxfr16zFt2rRqWznW1taYPn061q1b12TBEd2SXVCK6T/FQKFkeNbLAVJrurSszygf6I7fYu7hw1+v4GFhGZytTfDVeF8EdqrfyDqE1AflA9J4qhto6IZM3daghsXly5cRFBRU4/xhw4YhJiam0UER3bQmLB430vJgbSqivhUGgPKB9lMqGdYfvYX5v10BAIzp1hIn5w/CKN+WPEdG9A3lA0II8BTPsajtnjdjY2NkZmY2Oiiie3afS8HeC6kAgI2vd0c7J0ueIyLNjfKB9vv8SDw2RiYCAIZ3lmLV6M4wNqJRnkjTo3xAGovRsFB6oUG/MC1btsS1a9dqnH/lyhW1h84Qw/G/Y6oHEE3p546+bR14joZoAuUD7fbLhVSuURHYyQnfvtYd5vQsGdJMKB8QQoAGNixGjBiBxYsXo6SkpMq84uJiLF26FCNHjmyy4IhuyCuR435OMQBgXkA7nqMhmkL5QHsplQw7Hw8n28nFCute8YVAQKcBSfOhfEAaiy5Y6IcGnb5atGgR9u/fj3bt2mH27Nlo3749AODmzZvYsGEDFAoFPv7442YJlGiv4zczAAA2ZiJYm9LwcIaC8oH22hmdjMv3cmEqMsKm13vQlQrS7CgfEEKABjYsnJyccObMGcyYMQMhISGoeASGQCBAYGAgNmzYwD24hhiGorJyfPb3TQDAc13oMrchoXygnWKSH2LxoesAgHG9XOFqZ8ZzRMQQUD4gTYWuWOi2Bp/GcnNzw+HDh/Ho0SMkJCSAMQYvLy/Y2to2R3xEiymVDNN/isGD3BK0tDGlkaAMEOUD7aJUMnz+TzwAwFtqiffo1kSiQZQPSGPQ45r1w1NfH7e1tUWvXr2aMhaiQ5RKhhV/XcfJ21kAgC9e9oGJyIjnqAhfKB9oh30xqTh75yEkxkJseK07rM3o1kSieZQPCDFcdOMtabCicmDB/ms4eDkNADAvwAv+be15jooQw5aQkY9lv98AALw72BNtW1jwHBEhhNQfo0sWeoEaFqRBGGPYfNMId/JVjYpPxnTGa35uPEdFCPnjchqK5Qp4OVrgzWc8+A6HEEKeCg1gp9uoYUEa5MiNDNzJF8BMbIRtk3uhTxu6UkEI30rLFTh5W/XwsXG9XGEmptROCNEtdL1CP9CvD2mQS6k5AIDRvs7UqCBEC5y/+xAzfr6IrIJSGAkFeNarBd8hEUIIMVANekAeMWxyhRKnErIBAK1pCEtCtMK3xxOQVVAKAFj3ig/aSy15jogQQhqOuljoB7piQertp6hkxKcXQCxkGNyezooSwreM/BIcj1fdAvXLdH/09rDjOSJCCCGGjK5YkHrJKSrDVxG3AQBj3JXwcDDnOSJCSNg1GQCgS0tralQQQnQae9zLgvpu6zZqWJA6yXJLMP2nGOQWy9HeyQJ9HOl6JSHa4NeYewCAAe3oCiIhhBD+UcOC1Ioxhtm7LiI6SfXQraUjO0BIpxMI0Qq30vMBACN9nHmOhBBCGonOWeoFaliQWv1zPR0Xkh/BVGSEv+Y8i17utnyHRAgBkPqwCCVyJYyEArSypcEUCCGE8I86b5NqKZUMey+k4vN/4gEAk/q6w9PRAnK5nOfICCHAf/0rfF1tYCGhVE4I0W0VFyzopgjdRr9GpFqfHo7DllNJAAAvRwvMGNiW54gIIRVK5Ap8/XgwheGdpTxHQwghhKhoxa1QGzZsgLu7O0xMTODn54dz587VuOzmzZvx7LPPwtbWFra2tggICKiy/OTJkyEQCNReQUFBzV0MvXH2TjbXqJgf1B5/vPsMrE1FPEdFDAXlg7pFJz1Efmk5nKwkmNLPg+9wCGk2lA8MEF2y0Gm8Nyz27t2L4OBgLF26FBcvXoSPjw8CAwORkZFR7fKRkZGYMGECjh8/jqioKLi6umLYsGG4f/++2nJBQUFIS0vjXrt379ZEcXTerzH38OrmswCAsd1bYuZAT5iIjHiOihgKygf1c0um6rTdy90ORjSaAtFTlA8MCz0gTz/w3rBYt24dpk2bhilTpqBjx47YtGkTzMzMsG3btmqX37lzJ2bOnAlfX194e3tjy5YtUCqViIiIUFtOIpFAKpVyL1tb6nRclx+j7uKDfZehZKr7tpeO7MR3SMTAUD6on+ikhwCADs5WPEdCSPOhfGCY6FSJbuO1j0VZWRliYmIQEhLCTRMKhQgICEBUVFS91lFUVAS5XA47O/WHQ0VGRsLR0RG2trYYPHgwVq1aBXt7+2rXUVpaitLSUu59Xl4eAEAulxtMZ+XswjIs/f06AKCXuy22T+wOiQhVyl/x3lDqpSlpou50+e9C+UBdbdvLvUdFAACvFmY6/TdvDMpFddPlutH2fADodv1qI3l5+X//p7ptcpqqU14bFllZWVAoFHByclKb7uTkhJs3b9ZrHQsWLICLiwsCAgK4aUFBQRg7diw8PDyQmJiIjz76CMOHD0dUVBSMjKre1hMaGorly5dXmX7kyBGYmRnGMI5H7wvAmBGczRhek2YiIvyfWpcPDw/XUGT6pznrrqioqNnW3dwoH1Tvye1FVgTclBlDAIZ718/jcKLGQ9IqlIv0k7bnA4C2vaZ2M0cAwAgCUN3qMp0eFWr16tXYs2cPIiMjYWJiwk0fP3489/8uXbqga9euaNu2LSIjIzFkyJAq6wkJCUFwcDD3Pi8vj7s308pK/281yC+RY8X60wDK8OZAbzzn71bjsnK5HOHh4Rg6dChEIurQ3RCaqLvKZ9MMjb7lg5q2l0WHrgO4j4AOTnhjrK/G4tE2lIvqJpfLcejQIb7D4EVz5wMAtO01McuELGyMuwiA6rY5aCof8NqwcHBwgJGREdLT09Wmp6enQyqtfQjFL774AqtXr8bRo0fRtWvXWpdt06YNHBwckJCQUG3ikEgkkEgkVaaLRCKD2LDn74pFdmEZ7M3FeKlH63qV2VDqpjk0Z93p8t+E8kH1Kn9viVyBP66onl/x1rNtdPrv3VQoF+knbc8HAG17Tc3Y6L9DUqpb3cVr522xWIwePXqodayq6Gjl7+9f4+fWrFmDlStXIiwsDD179qzze+7du4fs7Gw4Ozs3Sdz6JjI+EwDw3Rs9YGsu5jkaYqgoH9TthzN3UVSmgNTKBD3d7er+ACE6ivKB4aFBofQD76NCBQcHY/Pmzfjhhx8QFxeHGTNmoLCwEFOmTAEATJw4Ua3z1meffYbFixdj27ZtcHd3h0wmg0wmQ0FBAQCgoKAAH374Ic6ePYu7d+8iIiICo0aNgqenJwIDA3kpoza7k1mAciWDUEAjzBD+UT6o3YFLqmEz3xvqRcPMEr1H+YAQ3cN7H4tx48YhMzMTS5YsgUwmg6+vL8LCwrgOWykpKRAK/2v/bNy4EWVlZXjppZfU1rN06VIsW7YMRkZGuHLlCn744Qfk5OTAxcUFw4YNw8qVK2u8nGnINp9UPQivc0trmEt43xyIgaN8ULOyciUSMlQHSM94teA5GkKaH+UDw8IeP8hCQOdMdJpWHEnOnj0bs2fPrnZeZGSk2vu7d+/Wui5TU1P880/tIxoRlbwSOX6PVZ0BndLPnd9gCHmM8kH1krIKUa5ksJAYw8XapO4PEKIHKB8Qolt4vxWK8OeX86koLFOgTQtzjPJpyXc4hJBanLyt6gvlLbWEgE7pEUL0DPWx0A/UsDBgm06oBsB/tXdrCOl+bUK0llLJ8NPZZADAmO50EoAQQoh2ooaFgUrOLkRWQRkA4MXurXiOhhBSm8TMAiRnF8FUZIQx3ahhQQjRX3SaU7dRw8IAFZcpsPC3qwCAzi2tYGNGY0UTos12RqcAADq5WMFMrBVd4wghpGnRvVB6gX6hDEhGfglW/HEDEXEZKJYrAADBQ9vR/dqEaLGHhWX4+fFtUDMHteU5GkIIIaRm1LAwEMVlCkzdcQFX7+cCAFramCJ4aDsM9nbiOTJCSG0upeagXMnQpoU57a+EEL3F6JKFXqCGhQEoVyix6OA1XL2fC1szEbZM6oXurW3oSgUhOuDYTdVoUH3b2vMcCSGEND86MtFt1LDQU7lFcly9n4vY1Ef45cI9pDwsAgAsHO6NHm62PEdHCKmvm7J8AEDftg48R0IIIc2H0QULvUANCz004+cY/H1NpjbN1kyE4GHt8UpPV56iIoQ0lJIBCZmFAIB2ThY8R0MIIc2PbqbQbdSw0DNbTt7hGhWOlhL09rBDnzb2GNu9JY0mQ4iOySoBisoUMBEJ4eFADQtCiP6iKxb6gY40dZhSyRB5KwNnErIRn56Pm7J8ZOaXAgAm9HZF6NiuPEdICGmMW7mqU3ftnSxhRA+xJIQQouWoYaGjkrIKMfWH87jz+DaJyqYPaIOFQd48REUIaUon0lSPGqLRoAgh+o4uWOgHaljooPS8EszaeRF3MgthZWKMF3xd0NnFGl5OlvBysoCVCT3wjhBdd+7uQ2SUCCAyEuANfze+wyGEEI2ga7O6jRoWOuR2ej6+//cOfr/8AKXlStiZi3F4zrOQWpvwHRohpIltOH4HAPBS95awMxfzHA0hhDQvRp0s9AI1LHRETlEZAtf/C+Xj/a6Hmy1WjOpEjQpC9AxjDEfjMnDmzkMAwMQ+rXmOiBBCCKkfaljogIz8Ery48QzXqNj5lh/6trWnB9wRood2Rqdg0cFrAIB21kq0bWHOc0SEENL86HqFfqCGhZYrKivH1B0XkPqwGK1sTfG/V7vD19WG77AIIc3g2v1crPrrBgAgqJMTBpnfpxMIhBBCdIaQ7wBI7TaduIOr93NhYybCz1P9qFFBiJ5Kzi7EtB8voESuRN+29lj7UheYGPEdFSGEaBadS9FtdMVCSxWXKRB2PQ07TicBAGYN9IS7A90SQYi+2hiZiLTcEjhZSbB+vC/ExnTehxBiOKjvtn6ghgXPUh8WISmrEGm5xbifU4IHOcV4kFOMSyk5KJYrAACejhZ41Y86cBKir/688gB7L6QCAD4Z3QWOliaQy+U8R0UIIYQ0DDUsNCinqAx3sgqRlFmIhMwCHIvLQHx6fo3Lt7I1xSs9XTHt2TYwFdM9EYToo7BraZi96xIA4FkvBwz2duQ5IkII0byK4WbpTijdphXX2jds2AB3d3eYmJjAz88P586dq3X5ffv2wdvbGyYmJujSpQsOHz6sNp8xhiVLlsDZ2RmmpqYICAjA7du3m7MINSoqK8ePUXfR59MI+K4Ix9hvz+D9fZexMTIR8en5MBYK0M7JAgPbt8Crfq3xYWB7rHvFB3+++wxOzh+EOUO8qFFBDIo+54PKyhVK7D2fghk7LwJQnUhY94ovhEL6WSWkgqHkAwKUKZQAACMB3ROly3i/YrF3714EBwdj06ZN8PPzw/r16xEYGIj4+Hg4OlY9c3fmzBlMmDABoaGhGDlyJHbt2oXRo0fj4sWL6Ny5MwBgzZo1+Prrr/HDDz/Aw8MDixcvRmBgIG7cuAETk6Z77kN2QSkeFpbhUZEcj4rKkFP03/9zi+TIyC9FVGI2d0sTAEitTODhYA6PFubwdbXBsI5OsDGjh18RAuh2PngSYwz5peXIyCtFZn4pMgtU/2bklyAzvxTRdx7ifk4xANWVis0Te8JERCcRCKmgT/mA1K1coWpQUPcy3SZgPD/q0M/PD7169cL//vc/AIBSqYSrqyveffddLFy4sMry48aNQ2FhIf78809uWp8+feDr64tNmzaBMQYXFxe8//77+OCDDwAAubm5cHJywo4dOzB+/Pg6Y8rLy4O1tTVyc3NhZWWlNq9cocTf12TYcioJl1Nz6lVGN3szvPWMB8Z2bwVzCe9tuUaRy+U4fPgwRowYAZFIxHc4OkUTdVfbtqsLdC0fAMCBS/eQkl2MzIKSxw2Hxw2J/FKUlitrXbeduRhv92+DN/t5VOmsTfta7ah+6iaXy/Hrr7/i1Vdf1cmcoM35YNeuXXjppZdo22tCu8+lIGT/VXS2VeJAcBDVbRPTVD7g9Si3rKwMMTExCAkJ4aYJhUIEBAQgKiqq2s9ERUUhODhYbVpgYCAOHjwIAEhKSoJMJkNAQAA339raGn5+foiKiqo2cZSWlqK0tJR7n5eXB0D1R3iyA2VcWj7e3a26H1ogAKxNRLAxe/wyFcHWTARrUxFszMSwMROha0srdHaxejwWPdP5DpkV8et6OfigibrT5b+LLuYDANhwLAEJmYU1lsvSxBgtLMRwsJCghYUELSxV/3e1NcVg7xaqqxRMAXmlK5sV31f5X6KO6qduulw32p4PAN2uX210/X4OAMBWTHXbHDRVp7w2LLKysqBQKODk5KQ23cnJCTdv3qz2MzKZrNrlZTIZN79iWk3LPCk0NBTLly+vMv3IkSMwMzOrMr27vRCOpsAzUiUsReUAiquutEj1Ss0CUi9X+7U6LTw8nO8QdFZz1l1RUVGzrbu56Wo+aCMWooUjYCUCLMUMViLA6vG/liJAbFQOoET9QwWq17HUakNQQ/ta7ah+9JO25wOAtr2mJnokgK+9AJ1sGdWtDtPt+3KaSEhIiNpZjry8PLi6umLYsGHVXioaMUKT0WkXuVyO8PBwDB06lC5TNpAm6q7y2TTydBqcD5opDtrXakf1Uze5XI5Dhw7xHYZOqykfAKBtr4mNAO3XzUlT+YDXhoWDgwOMjIyQnp6uNj09PR1SqbTaz0il0lqXr/g3PT0dzs7Oasv4+vpWu06JRAKJRFJlukgkog27BlQ3T685606X/yaUD6pH+1rtqH70k7bnA4C2veZEdau7eO17LxaL0aNHD0RERHDTlEolIiIi4O/vX+1n/P391ZYHVJcjK5b38PCAVCpVWyYvLw/R0dE1rpMQwj/KB4SQCpQPCNFNvN8KFRwcjEmTJqFnz57o3bs31q9fj8LCQkyZMgUAMHHiRLRs2RKhoaEAgLlz52LAgAFYu3YtnnvuOezZswcXLlzA999/DwAQCASYN28eVq1aBS8vL244ORcXF4wePZqvYhJC6oHyASGkAuUDQnQP7w2LcePGITMzE0uWLIFMJoOvry/CwsK4zlUpKSkQCv+7sNK3b1/s2rULixYtwkcffQQvLy8cPHiQG6MaAObPn4/CwkK8/fbbyMnJwTPPPIOwsLB6j1FdMQIv3a9elVwuR1FREfLy8ugyZQNpou4qtlmeR5F+apQP/kP7Wu2ofupWUUeAbuYEbc4HtO01D9qvm4+m8gHvz7HQRvfu3eM6ZxGii1JTU9GqVSu+w9ALlA+IPqCc0DQoHxB90Jz5gBoW1VAqlXjw4AEsLS0fP3+CVKgYESM1NVXnHrbEN03UHWMM+fn5cHFxUTuTR54eX/mA9rXaUf3UraKObty4gfbt21NOaAJKpRLx8fHo2LEjbXvNgPbr5qOpfMD7rVDaSCgU0pmdOlhZWdFO/5Sau+6sra2bbd2GiO98QPta7ah+6tayZUtqVDQRoVCIli1bAqBtrzlR3Taf5s4HlGkIIYQQQgghjUYNC0IIIYQQQkijUcOCNIhEIsHSpUtrfGAQqRnVHWkI2l5qR/VTN6qj5kH12nyobpuPpuqWOm8TQgghhBBCGo2uWBBCCCGEEEIajRoWhBBCCCGEkEajhgUhhBBCCCGk0ahhQQghhBBCCGk0aliQBtmwYQPc3d1hYmICPz8/nDt3ju+QtEpoaCh69eoFS0tLODo6YvTo0YiPj1dbpqSkBLNmzYK9vT0sLCzw4osvIj09naeIibaifU3l33//xfPPPw8XFxcIBAIcPHhQbT5jDEuWLIGzszNMTU0REBCA27dv8xMsDyjnNM6yZcsgEAjUXt7e3tz8gQMHVpn/zjvv1LrOyZMnV/lMUFBQcxdFq9RVrwAQFRWFwYMHw9zcHFZWVujfvz+Ki4trXS/lxeap2/qss76oYUHqbe/evQgODsbSpUtx8eJF+Pj4IDAwEBkZGXyHpjVOnDiBWbNm4ezZswgPD4dcLsewYcNQWFjILfPee+/hjz/+wL59+3DixAk8ePAAY8eO5TFqom1oX/tPYWEhfHx8sGHDhmrnr1mzBl9//TU2bdqE6OhomJubIzAwECUlJRqOlB+UcxqvU6dOSEtL416nTp1Smz9t2jS1+WvWrKlznUFBQWqf2b17d3OFr7Vqq9eoqCgEBQVh2LBhOHfuHM6fP4/Zs2fX+kRoyov/aeq6rWudDcIIqafevXuzWbNmce8VCgVzcXFhoaGhPEal3TIyMhgAduLECcYYYzk5OUwkErF9+/Zxy8TFxTEALCoqiq8wiZahfa16ANiBAwe490qlkkmlUvb5559z03JycphEImG7d+/mIUL+Uc5pmKVLlzIfH58a5w8YMIDNnTu3QeucNGkSGzVqVKPi0nV11aufnx9btGhRg9ZJeVGlOeq2rnU2BF2xIPVSVlaGmJgYBAQEcNOEQiECAgIQFRXFY2TaLTc3FwBgZ2cHAIiJiYFcLlerR29vb7Ru3ZrqkQCgfa0hkpKSIJPJ1OrK2toafn5+BltXlHMa7vbt23BxcUGbNm3w2muvISUlRW3+zp074eDggM6dOyMkJARFRUV1rjMyMhKOjo5o3749ZsyYgezs7OYKX2vVVK8ZGRmIjo6Go6Mj+vbtCycnJwwYMKDWM+SUF9U1Zd3Wtc6GooYFqZesrCwoFAo4OTmpTXdycoJMJuMpKu2mVCoxb9489OvXD507dwYAyGQyiMVi2NjYqC1L9Ugq0L5WfxX1QXWlQjmn4fz8/LBjxw6EhYVh48aNSEpKwrPPPov8/HwAwKuvvoqff/4Zx48fR0hICH766Se8/vrrta4zKCgIP/74IyIiIvDZZ5/hxIkTGD58OBQKhSaKpBVqq9c7d+4AUN3XP23aNISFhaF79+4YMmRIjf2jKC/+p6nrtq51NpTxU5eMEFKrWbNm4dq1a09/nyIhhDQA5ZyGGz58OPf/rl27ws/PD25ubvjll18wdepUvP3229z8Ll26wNnZGUOGDEFiYiLatm1b7TrHjx+v9pmuXbuibdu2iIyMxJAhQ5qvMFqktnrt0KEDAGD69OmYMmUKAKBbt26IiIjAtm3bEBoaykvMuqI56rau/aAh6IoFqRcHBwcYGRlVGUkkPT0dUqmUp6i01+zZs/Hnn3/i+PHjaNWqFTddKpWirKwMOTk5astTPZIKtK/VX0V9UF1RzmkqNjY2aNeuHRISEqqd7+fnBwA1zq9OmzZt4ODg0KDP6JvK9ers7AwA6Nixo9oyHTp0qPH2G8qLNWts3da1zoaihgWpF7FYjB49eiAiIoKbplQqERERAX9/fx4j0y6MMcyePRsHDhzAsWPH4OHhoTa/R48eEIlEavUYHx+PlJQUqkcCgPa1hvDw8IBUKlWrq7y8PERHRxtMXVHOaVoFBQVITEzkDtCeFBsbCwA1zq/OvXv3kJ2d3aDP6JvK9eru7g4XF5cqwyLfunULbm5u1X6e8mLNGlu3da2zwZqkCzgxCHv27GESiYTt2LGD3bhxg7399tvMxsaGyWQyvkPTGjNmzGDW1tYsMjKSpaWlca+ioiJumXfeeYe1bt2aHTt2jF24cIH5+/szf39/HqMm2ob2tf/k5+ezS5cusUuXLjEAbN26dezSpUssOTmZMcbY6tWrmY2NDTt06BC7cuUKGzVqFPPw8GDFxcU8R64ZlHMa5/3332eRkZEsKSmJnT59mgUEBDAHBweWkZHBEhIS2IoVK9iFCxdYUlISO3ToEGvTpg3r37+/2jrat2/P9u/fzxhTba8ffPABi4qKYklJSezo0aOse/fuzMvLi5WUlPBRRF7UVq+MMfbll18yKysrtm/fPnb79m22aNEiZmJiwhISErh1DB48mH3zzTfce8qLKs1Rt3WtsyGoYUEa5JtvvmGtW7dmYrGY9e7dm509e5bvkLQKgGpf27dv55YpLi5mM2fOZLa2tszMzIyNGTOGpaWl8Rc00Uq0r6kcP3682n1q0qRJjDHVkLOLFy9mTk5OTCKRsCFDhrD4+Hh+g9YgyjmNM27cOObs7MzEYjFr2bIlGzduHHcAlpKSwvr378/s7OyYRCJhnp6e7MMPP2S5ublq66hc30VFRWzYsGGsRYsWTCQSMTc3NzZt2jSDO/itrV4rhIaGslatWjEzMzPm7+/PTp48qTbfzc2NLV26VG0a5cXmqdv6rLO+BIwx1vDrHIQQQgghhBDyH+pjQQghhBBCCGk0algQQgghhBBCGo0aFoQQQgghhJBGo4YFIYQQQgghpNGoYUEIIYQQQghpNGpYEEIIIYQQQhqNGhaEEEIIIYSQRqOGBSGEEEIIIaTRqGGhpWQyGYYOHQpzc3PY2NjUOK057Nixo1nXr60iIyMhEAggEAgwevToWpcdOHAg5s2b16D13717l1u/r6/vU8dJiCFbtmwZnJycIBAIcPDgwRqnEUIMT8VvbF3HMMuWLdPY7/COHTu4uBp63KCLqGHBg8mTJ3MbWeVXUFAQt8yXX36JtLQ0xMbG4tatWzVOayx3d3esX79ebdq4ceOabP212bx5M3x8fGBhYQEbGxt069YNoaGh3Pwnd/xly5ZBIBDgnXfeUVtPbGwsBAIB7t69C0D9AF4gEMDOzg4DBgzAyZMn6xVXfHw8duzY0aCyDBw4UO07nZyc8PLLLyM5OZlbxtXVFWlpaXj//fcbtG5CmkpqairefPNNuLi4QCwWw83NDXPnzkV2drbacnU1nOt7AB8aGgojIyN8/vnnjYxcJS4uDsuXL8d3332HtLQ0DB8+vNppjUUNFKLNqjt+qPxatmwZDh8+DLFYjIsXL6p9du3atXBwcIBMJuMp+ua3ffv2Bh/DVBxfVLysra3x7LPP4sSJE42OZ9y4cUhLS4O/v3+j16ULqGHBk6CgIKSlpam9du/ezc1PTExEjx494OXlBUdHxxqnNQdTU9NmXT8AbNu2DfPmzcOcOXMQGxuL06dPY/78+SgoKKj1cyYmJti6dStu375d53ccPXoUaWlp+Pfff+Hi4oKRI0ciPT29zs85Ojo+1RWbadOmIS0tDQ8ePMChQ4eQmpqK119/nZtvZGQEqVQKCwuLBq+bkMa6c+cOevbsidu3b2P37t1ISEjApk2bEBERAX9/fzx8+LDJv3Pbtm2YP38+tm3b1iTrS0xMBACMGjUKUqkUEomk2mmE6LPKxw3r16+HlZWV2rQPPvgAI0aMwMSJEzFx4kSUlpYCAG7cuIFFixZhw4YNkEqlPJeiceRyeY3zbGxsnuoYplOnTlwdRkVFwcvLCyNHjkRubm5jQoWpqSmkUinEYnGj1qMrqGHBE4lEAqlUqvaytbUFoLqK8Ntvv+HHH3+EQCDA5MmTq50GADk5OXjrrbfQokULWFlZYfDgwbh8+bLad/3xxx/o1asXTExM4ODggDFjxgBQnZVMTk7Ge++9x7XSAfVboW7dugWBQICbN2+qrfPLL79E27ZtuffXrl3D8OHDYWFhAScnJ7zxxhvIysqqsfy///47XnnlFUydOhWenp7o1KkTJkyYgE8++aTWemvfvj0GDRqEjz/+uM46tre3h1QqRefOnfHRRx8hLy8P0dHRdX7uSYWFhZg4cSIsLCzg7OyMtWvXVrucmZkZpFIpnJ2d0adPH8yePbvK2SJC+DJr1iyIxWIcOXIEAwYMQOvWrTF8+HAcPXoU9+/fr9c+1RAnTpxAcXExVqxYgby8PJw5c6bOz1y9ehWDBw+Gqakp7O3t8fbbb3MnG5YtW4bnn38eACAUCrkzs09OA1S3Nfbu3Zu7bbRfv35qVw8PHTqE7t27w8TEBG3atMHy5ctRXl4OQJV/AWDMmDEQCATce0K0ReXjBmtrawgEArVpFSevvvzySxQUFGDp0qUoLy/HpEmT8Pzzz2PcuHE1rrvi9//gwYPw8vKCiYkJAgMDkZqaqrbcxo0b0bZtW4jFYrRv3x4//fQTN++DDz7AyJEjuffr16+HQCBAWFgYN83T0xNbtmzh3m/ZsgUdOnSAiYkJvL298e2333LzKu5C2Lt3LwYMGAATExPs3LmzQXW2evVqODk5wdLSElOnTkVJSUmVZYyNjbk67NixI1asWIGCggK1qx8CgQDfffcdRo4cCTMzM3To0AFRUVFISEjAwIEDYW5ujr59+3InPAwRNSy00Pnz5xEUFIRXXnkFaWlp+Oqrr6qdBgAvv/wyMjIy8PfffyMmJgbdu3fHkCFDuLOPf/31F8aMGYMRI0bg0qVLiIiIQO/evQEA+/fvR6tWrbBixQqulf6kdu3aoWfPnlV24p07d+LVV18FoGrcDB48GN26dcOFCxcQFhaG9PR0vPLKKzWWUSqV4uzZs2o/9vW1evVq/Pbbb7hw4UK9li8uLsaPP/4IAE91xuDDDz/EiRMncOjQIRw5cgSRkZF1NhgePnyIX375BX5+fg3+PkKa2sOHD/HPP/9g5syZMDU1VZsnlUrx2muvYe/evWCMNdl3bt26FRMmTIBIJMKECROwdevWWpcvLCxEYGAgbG1tcf78eezbtw9Hjx7F7NmzAagOVrZv3w4Aamdmn5xWXl6O0aNHY8CAAbhy5QqioqLw9ttvc42OkydPYuLEiZg7dy5u3LiB7777Djt27OBOapw/fx6A6naKtLQ07j0husbS0hLbtm3D2rVr8dprryE1NRUbN26s83NFRUX45JNP8OOPP+L06dPIycnB+PHjufkHDhzA3Llz8f777+PatWuYPn06pkyZguPHjwMABgwYgFOnTkGhUABQnWRwcHBAZGQkAOD+/ftITEzEwIEDAaiOJ5YsWYJPPvkEcXFx+PTTT7F48WL88MMPanEtXLgQc+fORVxcHAIDA+tdD7/88guWLVuGTz/9FBcuXICzs7Naw6U6paWl2L59O2xsbNC+fXu1eStXrsTEiRMRGxsLb29vvPrqq5g+fTpCQkJw4cIFMMa4vGWQGNG4SZMmMSMjI2Zubq72+uSTT7hlRo0axSZNmqT2uSennTx5kllZWbGSkhK15dq2bcu+++47xhhj/v7+7LXXXqsxFjc3N/bll1+qTdu+fTuztrbm3n/55Zesbdu23Pv4+HgGgMXFxTHGGFu5ciUbNmyY2jpSU1MZABYfH1/t9z548ID16dOHAWDt2rVjkyZNYnv37mUKhYJbZunSpczHx6fa9+PHj2eDBw9mjDF26dIlBoAlJSUxxhhLSkpiAJipqSkzNzdnAoGAAWA9evRgZWVlNdbF8ePHGQD26NEjblp+fj4Ti8Xsl19+4aZlZ2czU1NTNnfuXG7agAEDmEgkYubm5szMzIwrV0VMlT1ZLkKa29mzZxkAduDAgWrnr1u3jgFg6enpjDHV9lx5+35SbetijLHc3FxmamrKYmNjGWOqfdTCwoLl5+fX+Jnvv/+e2drasoKCAm7aX3/9xYRCIZPJZIwxxg4cOMCe/Nl6clp2djYDwCIjI6v9niFDhrBPP/1UbdpPP/3EnJ2d610+QrTFk7/X1Rk/fjwDwPbu3Vuv9QFgZ8+e5abFxcUxACw6Opoxxljfvn3ZtGnT1D738ssvsxEjRjDGGHv06BETCoXs/PnzTKlUMjs7OxYaGsr8/PwYY4z9/PPPrGXLltxn27Zty3bt2qW2vpUrVzJ/f3/G2H+/6evXr68z/ur2XX9/fzZz5ky1aX5+flWOL4RCIXc8JhAImJWVFfv777+rrH/RokXc+6ioKAaAbd26lZu2e/duZmJiUiW2uvKqvqArFjwZNGgQYmNj1V5Pdkquy+XLl1FQUAB7e3tYWFhwr6SkJO4yXGxsLIYMGdKoWMePH4+7d+/i7NmzAFRnF7p37w5vb28ujuPHj6vFUDGvpsuBzs7OiIqKwtWrVzF37lzuMm1QUBCUSmWdMa1atQonT57EkSNHalxm7969uHTpEn777Td4enpix44dEIlEDSp7YmIiysrK1K482NnZVTmDAQCvvfYaYmNjcfnyZZw6dQqenp4YNmwY8vPzG/SdhDQX1oRXJGqze/dutG3bFj4+PgAAX19fuLm5Ye/evTV+Ji4uDj4+PjA3N+em9evXD0qlEvHx8fX+bjs7O0yePBmBgYF4/vnn8dVXX6ldjb18+TJWrFihlq8q+kcVFRU9RWkJ0V73799HWFgYzMzM6j2AibGxMXr16sW99/b2ho2NDeLi4gCo9tV+/fqpfaZfv37cfBsbG/j4+CAyMhJXr16FWCzG22+/jUuXLqGgoAAnTpzAgAEDAKiuVCYmJmLq1Klq++SqVauqHD/07NnzqeogLi6uyt0D1XWkbt++PXc8FhMTgxkzZuDll1+ucndE165duf87OTkBALp06aI2raSkBHl5eU8Vr64z5jsAQ2Vubg5PT89GraOgoADOzs7c5cXKKvpIPHnbw9OQSqUYPHgwdu3ahT59+mDXrl2YMWOGWhzPP/88PvvssyqfdXZ2rnXdnTt3RufOnTFz5ky888473CgMgwYNqvVzbdu2xbRp07Bw4cIab7FwdXWFl5cXvLy8UF5ejjFjxuDatWvN1rnT2tqa+5t6enpi69atcHZ2xt69e/HWW281y3cSUh+enp4QCASIi4vj+lhVFhcXB1tbW7Ro0aJJvm/r1q24fv06jI3/+4lRKpXYtm0bpk6d2iTfUZvt27djzpw5CAsLw969e7Fo0SKEh4ejT58+KCgowPLlyzF27NgqnzMxMWn22AjRpGnTpqFHjx74+OOPMXToULz00kvcQX1zGjhwICIjIyGRSDBgwADY2dmhQ4cOOHXqFE6cOMGNjljRh2rz5s1VDv6NjIzU3lc+6dAcxGKx2nFZt27dcPDgQaxfvx4///wzN73yCcqKWyyrm1afk6T6iK5Y6LDu3btDJpPB2NgYnp6eai8HBwcAqpZ1REREjesQi8XcfZC1qbgHOyoqCnfu3FG737J79+64fv063N3dq8TRkETQsWNHAKozGPWxZMkS3Lp1C3v27Klz2ZdeegnGxsZ13lf5pLZt20IkEql1+n706FG9hrKrSIrFxcUN+k5Cmpq9vT2GDh2Kb7/9tsr2KJPJsHPnTowbN477QWyMq1ev4sKFC4iMjFS7IhsZGYmoqKgqA0FU6NChAy5fvqy2/58+fRpCobDaK4R16datG0JCQnDmzBl07twZu3btAqDKV/Hx8VVylaenJ4RC1U+iSCSqV14kRJtt2bIFp06dwtatWzFo0CDMmDEDb775Zp2/seXl5Wpn6ePj45GTk4MOHToAUO2rp0+fVvvM6dOnud9w4L9+FhEREVxfioEDB2L37t24desWN83JyQkuLi64c+dOlf3Rw8OjCWpBFe+TA7dU3IFRFyMjI/oNbyBqWPCktLQUMplM7VXbKErVCQgIgL+/P0aPHo0jR47g7t27OHPmDD7++GMuKSxduhS7d+/G0qVLERcXh6tXr6pdWXB3d8e///6L+/fv1/r9Y8eORX5+PmbMmIFBgwbBxcWFmzdr1iw8fPgQEyZMwPnz55GYmIh//vkHU6ZMqfHHecaMGVi5ciVOnz6N5ORknD17FhMnTkSLFi3qPdazk5MTgoOD8fXXX9e5rEAgwJw5c7B69eoG3e5gYWGBqVOn4sMPP8SxY8dw7do1TJ48mTsAqayoqIj7W16+fBkzZsyAiYkJhg0bVu/vI6S5/O9//0NpaSkCAwPx77//IjU1FWFhYRg6dChatmxZZUS2zMzMKrdrVh6uOSkpqcr8wsJCbN26Fb1790b//v25K5KdO3dG//790atXrxqvML722mswMTHBpEmTcO3aNRw/fhzvvvsu3njjDe52g/pISkpCSEgIoqKikJycjCNHjuD27dvcQdGSJUvw448/Yvny5bh+/Tri4uKwZ88eLFq0iFuHu7s7IiIiIJPJ8OjRo4ZUMyFaITk5GcHBwfjiiy/g5uYGAPjss88gEAiwcOHCWj8rEonw7rvvIjo6GjExMZg8eTL69OnDDfzy4YcfYseOHdi4cSNu376NdevWYf/+/fjggw+4dfTv3x/5+fn4888/1RoWO3fuhLOzM9q1a8ctu3z5coSGhuLrr7/GrVu3cPXqVWzfvh3r1q1rkrqYO3cutm3bxj3fYunSpbh+/XqV5crLy7nf8Nu3b2PVqlW4ceMGRo0a1SRxGAy+O3kYokmTJjEAVV7t27fnlqlP523GGMvLy2Pvvvsuc3FxYSKRiLm6urLXXnuNpaSkcMv89ttvzNfXl4nFYubg4MDGjh3LzYuKimJdu3ZlEomE6wBZU2ewV155hQFg27ZtqzLv1q1bbMyYMczGxoaZmpoyb29vNm/ePKZUKqutg19//ZWNGDGCOTs7M7FYzFxcXNiLL77Irly5wi1TW+ftCrm5uczBwaHaztuXLl1SW7awsJDZ2tqyzz77rNqYquu8zZiqA/frr7/OzMzMmJOTE1uzZk2VTlgDBgxQ+1va2tqyAQMGsGPHjlX5Huq8Tfhy9+5dNmnSJObk5MTli3fffZdlZWWpLffk9lzxWrlyJWOMVTsPADtx4gSzt7dna9asqfb7P/vsM+bo6FjjIApXrlxhgwYNYiYmJszOzo5NmzZNrcN3fTpvy2QyNnr0aC63uLm5sSVLlqgNDBEWFsb69u3LTE1NmZWVFevduzf7/vvvufm///478/T0ZMbGxszNza1+lUsID6r7vVYqlWzIkCFVBlVhTDXoi5GRUY2DG1Ss77fffmNt2rRhEomEBQQEsOTkZLXlvv32W9amTRsmEolYu3bt2I8//lhlXT4+PkwqlXLvs7OzmUAgYOPHj6+y7M6dO7njFFtbW9a/f3+2f/9+xljNv+nVQQ0DL3zyySfMwcGBWVhYsEmTJrH58+dXOb6onMvMzMxYly5d2MaNG2tdf3Wx1XQsYSidtwWMaag3HyFaLjIyEoMGDcKjR4+e6gF59bVs2TIcPHgQsbGxzfYdhBBCSEPt2LED8+bNQ05ODt+hPBWBQIADBw5g9OjRfIdSxcCBA+Hr64v169fzHUqzoluhCHlCq1atMGHChCZfb0pKCiwsLPDpp582+boJIYQQAkyYMAGtWrXiOwzOzp07YWFhUe9RuXQdjQpFyGN+fn64ffs2AHBPLm1KLi4u3FWK5hqZihBCCDFUFb/hT44oxacXXniBG/GqOe+G0BZ0KxQhhBBCCCGk0ehWKEIIIYQQQkijUcOCEEIIIYQQ0mjUsCCEEEIIIYQ0GjUsCCGEEEIIIY1GDQtCCCGEEEJIo1HDghBCCCGEENJo1LAghBBCCCGENBo1LAghhBBCCCGNRg0LQgghhBBCSKNRw4IQQgghhBDSaNSwIIQQQgghhDQaNSwIIYQQQgghjWbMdwDaSKlU4sGDB7C0tIRAIOA7HELqjTGG/Px8uLi4QCik8wZNgfIB0WWUE5oW5QOiyzSRD6hhUY0HDx7A1dWV7zAIeWqpqalo1aoV32HoBcoHRB9QTmgalA+IPmjOfEANi2pYWloCUFW8lZUVz9HUTS6X48iRIxg2bBhEIhHf4Tw1Kkfj5eXlwdXVlduGSeNpIh/oy7ZfGZVJO1BOaFq6dnxQQRe3XU0xpLrRRD6ghkU1Ki5vWllZ6UTikMvlMDMzg5WVlU7vFFSOpkOX6JuOJvKBNmwzTY3KpF0oJzQNXTs+qKDL225zM8S6ac58wOsNl//++y+ef/55uLi4QCAQ4ODBg3V+JjIyEt27d4dEIoGnpyd27NhRZZkNGzbA3d0dJiYm8PPzw7lz55o+eEJIk6J8QAipjHICIbqH14ZFYWEhfHx8sGHDhnotn5SUhOeeew6DBg1CbGws5s2bh7feegv//PMPt8zevXsRHByMpUuX4uLFi/Dx8UFgYCAyMjKaqxiEkCZA+YAQUhnlBEJ0D6+3Qg0fPhzDhw+v9/KbNm2Ch4cH1q5dCwDo0KEDTp06hS+//BKBgYEAgHXr1mHatGmYMmUK95m//voL27Ztw8KFC5u+EIRoQGm5AjlFcjwqKkNOkRyudmZoaWPKd1hNivIBMXRKJYNcqYRCySBXMCiUDOUKJcqVDOUKhnJl9f+3MROhnZP+9aGgnEAMhVLJUK5kUDLVfq9gDAqF6l9lxXvlfy/VckC5UgmlEtx8JWMQCgTo4WbLW1l0qo9FVFQUAgIC1KYFBgZi3rx5AICysjLExMQgJCSEmy8UChEQEICoqKga11taWorS0lLufV5eHgDVfXdyubwJS9A8KmLUhVhrY+jlKCgtx87oVNxIy3vciJAjt1iOnGI5isoUast+PKI9Jvu71fjdhkCf8oG+bPuVNXWZKn54y5888H48TXWwrTrQVlQ6QOemK5VQKBjkyicO2CteiifXq3xifQzy8nIkpwhx/NcrUDJBzd9TXcPgie+p/L7i/4w9Xd0M6+iIDRN8q52nT9tUXZojJ+j68UEFfcwx1WGMoUzBUFymQLFcwf1bVOnfkifeF5XIEZ8kxInfrkLJ8Phg/vFBOwN3oK9UVjroV7L/5qkd7DP15VkNjQal+jqaUgsLMc4sGFjtPE38/XWqYSGTyeDk5KQ2zcnJCXl5eSguLsajR4+gUCiqXebmzZs1rjc0NBTLly+vMv3IkSMwMzNrmuA1IDw8nO8QmoQhluPGIwF2JwqRJ6+5Q5UADGbGgLkxkHDzBg4/ul5lmaKioqeKVRfpYz7Ql22/ssaWKacU2JkoxK1cbXkGgxDIkGns2wRgEAoAo8cv4RP/GgmA4mwZDh8+XO3nKSc0Lifoy/FBBW3OMQoGPCoFMooFyCkDypRAmQIoVQogV6jelz7+VzVPwC3z3zRAiafpmCwEZGlNXqamUJEDhAAEj/d9IR7/K3g8rdJ7C0Epr/lApxoWzSUkJATBwcHc+4rhuIYNG6YToz7I5XKEh4dj6NChOj2igaGVIzO/FFF3HuLk7Swcik8DY0BrO1NM6OWKFhZi2JiJYGMmho2pCDZmIlhKjCEU1p4wK86mkafHRz7Ql22/ssaWqbhMgbVHb2PX5VTIFdWf0RMIAGOhQPUyEnL/N3rifcV8I6EAIqPH84WP5z9+LxIKH39OUGm6ECJufQIIGENyUiLat/OCRGRc+/cIBTAyqt/3VCxTXXx17fN1oZzQOLp+fFBBG3MMYwwXU3KwN+Y+LqfmIPVRcY37+tMQGQlgKjJSvcSqf83E//2/YrqJEZB2LwUd23ty+7VQABg93ieNBKr9sOJf48rzK0/j3gNGAgH3eaHgv/Vw02pYRiiovC7VdzTlCE6ayAc61bCQSqVIT09Xm5aeng4rKyuYmprCyMgIRkZG1S4jlUprXK9EIoFEIqkyXSQSac0OWB+6Fm9N9L0cxWUKfBZ2Ez+fTUZ5pUugE3q7YunznWAiMmrUdxoKfcwH+rLtV1ZTmRhjyCmSIz2/BOl5pUjPK0FGXgkSMwsRl5aH2xkF3C0CPd1s8fFzHdDW0eLxAXzTHHQ3lFwux+HDCRgx0FNn/k66EmdTaI6coC/HBxW0IW6lkuHXmHvYdjoJN2X5avMkxkK425ujpa0pzCXGMKtoFIiNuP+biY1hKhbCVGQMM/F/jQUzsTHXWDATG0FkVL+rnKr9+i5GDNKd/fppaaJ8OtWw8Pf3r3J5Jzw8HP7+/gAAsViMHj16ICIiAqNHjwYAKJVKREREYPbs2ZoOlxA15QolHuSUYMbOGFx/oDpr0MnFCn3b2qN/uxZ4xtOBxppvAMoHuin1YRHe/+UyYu/loKxcWeuyDhYSrBrdCUGdnTUUHdFllBO0X3ZBKWbvuoSoO9kAABOREC/4uOC5ri7wdLSAs5WJxk8YkKbFa8OioKAACQkJ3PukpCTExsbCzs4OrVu3RkhICO7fv48ff/wRAPDOO+/gf//7H+bPn48333wTx44dwy+//IK//vqLW0dwcDAmTZqEnj17onfv3li/fj0KCwu5ESAI0ZTUR0U4GCvDxZQcpDwswv2cYu4MrMRYiO/e6IGB7R15jlJ7UD7QH2XlSjzIKUZyPhBxMwOPihXIyi9FVkEpwq7LkJ73X2dYWzMRnKxM4GhlAidLCVztzNDB2QodnC3R0saUGtsGjHKCfknKKsT0ny7gVnoBzMRGmDvEC+N7tYa1mX5fJTA0vDYsLly4gEGDBnHvK+5jnDRpEnbs2IG0tDSkpKRw8z08PPDXX3/hvffew1dffYVWrVphy5Yt3DByADBu3DhkZmZiyZIlkMlk8PX1RVhYWJXOWoQ0pzPpArz35Sk8OdiD2FgIV1tTTO/flhoVT6B8oB+O3UzHvD2xyCspB2AMXIutsoyXowX+92p3uNmbNerWP6LfKCfohwc5xVgTdhOHLj8AY4CZ2AgHZvZDe6n+DZFMAAFjTzvAnf7Ky8uDtbU1cnNzdaJzlur+wMMYMWKETt8fqA/lSMkuQsj+KzidqLrM27mlFV73c0ObFhZobWcGR0tJs17m1bVtVxdook71Yduv4LviCHKKVEMa2ogZXFtYw9HSBA4WEjhYiuFiY4rnfVxgZaJ75dTFvxPlhKalq/XJ17YbfiMdwXtjkV9aDgAY7O2IeQFe6NrKRmMx1EUX9+unpYntV6f6WBCizUrkCoz7PgppuSUQChjG9XTFitFd6t2BjBBdU1hajqSsQiRmFiAxsxBRiVlco+LE+88i9sxxjBjRR+9/rAkh6vJK5Fj2+3UcuHQfjAG+rjZYNbozOre05js00syoYUFIE/kx6i7ScktgJBTgI59yTHyhIzUqiN5RKBl+q2FElwr927WA1MpEw5ERQvh271ER9l24h19j7uF+TjEA1YiHK0d1hjH9HhoEalgQ0gQupjzC+qO3AQALg9rBoZqH1xGiD+btjcUflx9w7+3MxWjjYI42LczRuaU1Bns7opWtmd4/4ZcQou7C3Yd468cL/90KaSbC5ok90cvdjufIiCZRw4KQRrqVno9XN59FiVwJH1cbTOjZChHh1LAg+iW/RI5lv9/gGhULgrzxSs9WsLeoOsY/IcRwFJcpsOLP69h9LhUA0KaFOWYO9MTQDk404pMBooYFIY3AGMPKP2+gRK5EnzZ22Da5F0QCGg+B6JeM/BJM2nYecWmq568M8XbEjIFteY6KEMK3coUSK/68wTUqXunZCh+P6EgNCgNGDQtCGmHzyTs4eTsLALBweAeYiY3pFhCid4L3XkZcWh7szcVY81JXDKKhkgkxWIwxfBuZiOM3M3DtQS5K5KoHXW6e2BNDO9KwvYaOGhaEPKXC0nJ8c0z18KYlIzvC19WG34AIaQZnErNwKkHVeF77ig89f4UQAyVXKBEvy8ePUXfxy4V73HQLiTGmPuNBjQoCgBoWhDy1X2PuIb+kHG0czDG5rzvf4RDSLP66kgYA6NbahhoVhBiIwtJyXLufi1sZBbgly8dNWR6u3v/v6gQABHWS4oPAdmjjYNGsz2ciuoUaFoQ8hUeFZfjiSDwA4LmuzpRUiV6KTc3BzmjVk40DO0l5joYQognFZQoMWXsCsrySKvOsTIzR3c0Wk/u6Y0C7FhAI6LePqKOGBSFP4beLqqsV7vZmeGcAdWIl+qWgtBwvbTzDPadCbCzEJH93foMihGjEVxG3uUbFYG9HeDlZoJ2jJXxcrenqBKkTNSwIaaCC0nKsPXILAPCGvzvMJbQbEf2RkFGA8d9HIaugDIDqYXfTnvWAqdiI58gIIc1tV3QKNp1IBAAse74jJvfz4DkiomvoiIiQBrr/qBjFcgUkxkLqW0H0zqKDV7lGxYIgbxpWlhA9pVAy/HU1Ddfv5yIpqxB3swtxK70AANC5pRXG9WrNc4REF1HDgpAGSnlYBABwtTODEV0SJnokLi0PZ+88BAB8Nd4Xo3xb8hwRIaQ5ZOSX4J2fYnAxJafKvNZ2Zjgwsx9ERkLNB0Z0HjUsCGmgiicP92trz3MkhDSt+4+KAQCtbE2pUUGInlIoGSZvO48baXmwlBhjbPeW8HAwh0cLC3jYm6OlrSmdNCNPjRoWhDTA75cf4PBV1fCbY7q34jkaQpoGYwzf/3uHG+nMwULCc0SEkOby761M3EjLg5WJMQ7NfgYeDuZ8h0T0CDUsCKkHxhh2Rqdg0cFrAIARXaTwaWXNc1SENI31R2/jq4jbAIDeHnb4eEQHniMihDSX2NQcAEBABydqVJAmRw0LQuphX8w9rlHxep/WWPp8Jxq/m+iFBznFXKNi7hAvvDe0Hc8REUKaS35JOX6IugsA8HG14TUWop+oYUFIPZy4lQlANab3ihc60zjeRG8s/f06AMDF2gRzhnjxHA0hpDldSH6EnCI5WtqYYlwvV77DIXqIuvwTUof8Ejmu3ssFAIzt3pIaFURvxCQ/QviNdADAopEdqcMmIXru3uMBGjo4W8FERM+mIU2PGhaE1CFk/1WkPCyCg4UYfds68B0OIU3ifk4x3v8lFgDQtZU1hneW8hsQIaRZPSoFvghX3fboLbXkORqir+hWKEJqkV8i587ofvGyD+zMxTxHREjjxMvy8f2/d/D75fuQKxgcLCRY81JX6jNEiJ77OUGIojIFBALAr40d3+EQPaUVVyw2bNgAd3d3mJiYwM/PD+fOnatx2YEDB0IgEFR5Pffcc9wykydPrjI/KChIE0UhemZXdApKy5Vo28Ic/b1a8B2OQaB80HwOxd5H4Pp/8dvFe5ArGPw87PDbDH94S634Do2QalE+aBrXH+QhIU91yLfxtR54ln7PSDPh/YrF3r17ERwcjE2bNsHPzw/r169HYGAg4uPj4ejoWGX5/fv3o6ysjHufnZ0NHx8fvPzyy2rLBQUFYfv27dx7iYTGZScNk1cix87oFADAm894UN8KDaB80Hx+jLqLJYeuc+8PzuoHXxoVhmgxygdNI/xGOubtvQQA8LA3w8D21KggzYf3Kxbr1q3DtGnTMGXKFHTs2BGbNm2CmZkZtm3bVu3ydnZ2kEql3Cs8PBxmZmZVEodEIlFbztbWVhPFIXqCMYZ3fopBysMimImNMLSjE98hGQTKB83nlwupAIBurW1w5L3+1KggWo/yQeNdSnmEt3+6gMJSBTytGH6d7kedtkmz4vWKRVlZGWJiYhASEsJNEwqFCAgIQFRUVL3WsXXrVowfPx7m5uoPeYmMjISjoyNsbW0xePBgrFq1Cvb29tWuo7S0FKWlpdz7vLw8AIBcLodcLm9osTSuIkZdiLU22lSOS6k5OJOYDbGxED9M7gFbE6N6x8VnObSh7p6WIeeD5t5mrtzLxbX7qnJ8MNQTHnYmzb6taNP+3FR0sUy6FGtlhpwPmkpiZiEWH7wGxoBn2tphjEMGTI11d5toLrq4Xz8tTZSR14ZFVlYWFAoFnJzUzwY7OTnh5s2bdX7+3LlzuHbtGrZu3ao2PSgoCGPHjoWHhwcSExPx0UcfYfjw4YiKioKRUdWWemhoKJYvX15l+pEjR2BmZtbAUvEnPDyc7xCaBN/lyCoB/nfdCIAAna3L8eDqGTy42vD18FGOoqIijX9nU6F80HzbzB/JQgBCOJkyZF4/i8M3muVrqsX3/twcdKlMupoTKB80jpIBn1wyQlapAGIhQz+LDBgLdWvb1TRDqBtN5APe+1g0xtatW9GlSxf07t1bbfr48eO5/3fp0gVdu3ZF27ZtERkZiSFDhlRZT0hICIKDg7n3eXl5cHV1xbBhw2Blpf2dGuVyOcLDwzF06FCIRCK+w3lq2lKOidsv4FHZQ7S2M8WXk3vCxca0QZ/nsxwVZ9MMkS7ng+bcZkrkCoSsjgSgwOiebfDcMM08BE9b9uempItlMtScoMv5oClcTMlB1tlzEBsLcXh2X7hYiXRu29UUXdyvn5Ym8gGvDQsHBwcYGRkhPT1dbXp6ejqk0trHVC8sLMSePXuwYsWKOr+nTZs2cHBwQEJCQrWJQyKRVNt5SyQS6dRGpmvx1oTPchSXKXDl8cPw1rzkA7cWT//DwUc5dPnvT/mgeb5j7dFEFJUpAADvBrSDSKTZtK8veakyXSqTrsT5JMoHjXMkLhMA8FwXZ3hKrblbYLQ9bj4ZQt1oony8dt4Wi8Xo0aMHIiIiuGlKpRIRERHw9/ev9bP79u1DaWkpXn/99Tq/5969e8jOzoazs3OjYyb663Z6PoZ/9S8KyxSwNxdT51YNo3zQ9G6n52PTiUQAwJzBnrCQ6PRFamJAKB80zuXUHABA/3b0UFeiWbyPChUcHIzNmzfjhx9+QFxcHGbMmIHCwkJMmTIFADBx4kS1zlsVtm7ditGjR1fpcFVQUIAPP/wQZ8+exd27dxEREYFRo0bB09MTgYGBGikT0U1Lf7+Ou9lFsDcX49vXutPIGTygfNB0SuQKvLolGgDg6WiBmYM8eY6IkIahfPB0Uh8W4dLjhoWXIz1hm2gW76evxo0bh8zMTCxZsgQymQy+vr4ICwvjOmylpKRAKFRv/8THx+PUqVM4cuRIlfUZGRnhypUr+OGHH5CTkwMXFxcMGzYMK1eu1PuxqsnTOxR7H2cSswEAu6b1QXspJWM+UD5oOg9yipGZXwoTkRD7pvtTQ5noHMoHT+ef6zIolAw+razRyUV7+4EQ/cR7wwIAZs+ejdmzZ1c7LzIyssq09u3bgzFW7fKmpqb4559/mjI8oufKypUIPawaZeT1Pq2pUcEzygdN42JKDgBAYmwEW3Mxv8EQ8pQoHzTcrnOqB7u+3NMVAgE92JVoFu+3QhHCJ8YYvoq4BVleCRwtJVg8siPfIRHSaGfvZOPDXy8DAF7wceE5GkKIpuSVyJGcrRpSdLB31aeTE9LcqGFBDNqRG+nYcFzVuXXWIE9IjOl2EaLbbqXn452fY8AY0L9dCyx7oRPfIRFCNOTXC/egUDJ4OVrA2dqE73CIAdKKW6EI4cs/12QAgFd6tsJEfzeeoyHk6Z2/+xCr/76JmORH3LTurW1gJKRbIQgxBHezCvHFkXgAwITerek2KMILalgQg1VcpsDRONUY6c91daEkTHRWabkCE7eeQ7Fc9byKwd6OGNrRCS92b8VzZIQQTVAqGebtjUVRmQJ25mKM7Kpfw+cS3UENC2KwjtyQIa+kHFIrEzzrSWN9E931U1Qy16gIf68/vJxoAAJCDMnP0cmITc2BxFiIX9/xh6MV3QZF+EF9LIhBOnw1DR/sU3Vufd7HGUK6XYTosCPXVVfexvdypUYFIQZo26kkAMCCIG+0aWHBczTEkNEVC2JQFEqGH6PuYtVfcVAoGQI7OeG9oe34DouQp3YrPR/n7j4EAAR2lvIcDSFE05KyCnH38UhQY7u35DkaYuioYUEMSujhOGx5fGZnbPeW+PwlH+rcSnTav7cyAQAu1iYY1J6GlyTE0ITfUA1C0tvDDjZm9Mwawi9qWBCDEZP8ENtOqxoV7wW0w7uDPekWKKLT/rkuw6q/4gAAL/agjtqEGJqyciV2RqseiEfPrSDagBoWxGDsv3gfSgY84+mAOUM8aRQootPKypVYdPAaAGBg+xZ4L4Bu6SPE0Gw7nYTk7CIYCwUY0ZlGgiL8o87bxCAUlJbjwKX7AICpz3hQo4LovHNJD5GZXwoAWPFCZ7r6RogBunBX9dyadwd7obW9Gc/REEINC2Igrt7LRVGZAg4WEgxs34LvcAhpNLlCCQDo3NKKDigIMUCMMdzJLAAAeDnRSFBEO1DDghiE0wlZAICebrZ0tYLohVOPt2kj2p4JMUi5xXLcySoEAPi3sec5GkJUqI8FMQiHr6YBAAbQ1Qqi4xhj+ONKGrY+Ht1sEHXYJMQg3UpXXa2wlBjD1pxGgyLaoUFXLCZOnIj8/Hzu/eXLlyGXy5s8KEKaUmZ+KXdW51kvesJ2U6F8wI+zdx5izu5LAAB7czHeGdCW54gIoXzAh/h0VX33dLflORJC/tOghsXOnTtRXFzMvX/22WeRmpra5EER0pT+uvIAANDB2QotbUx5jkZ/UD7gR05RGQDAy9ECx94fCBOREc8REUL5gA9XUnMAAC70u0a0SIMaFoyxWt8Too1iHyffoE5S6l/RhCgf8MvWTAxrMxHfYRACgPIBHxIed9zu50lX4on2oM7bRK9du5+Lk7dVnVw7OFvyHA0hjRcZr3rSNrWRCTFc5QolbqapboVytzfnORpC/tPgzts3btyATKZ6fDxjDDdv3kRBQYHaMl27dm2a6AhphGv3c/HKd1EoKlPAy9ECz3pRx+2mRvlAsxhj3PNYnutKD8Mi2oXygeZkFZShWK6AkVAAbymdNCPao8ENiyFDhqhd4hw5ciQAQCAQgDEGgUAAhULRdBES0kByhRJ7z6diTdhNFJUp4Odhh+8n9oSpmO5Fb2qUDzQrMj4TZY+fXzG6W0ueoyFEHeUDzal4OKaDhZgejkm0SoMaFklJSc0VByFNZsmha9h9TtVp0MfVBt++1h3WpnQvelOjfKB5v1xQbdcD27eAlQlt00R7UD7QrMyCEgBAC0sJz5EQoq5BDQs3N7fmioOQJnEo9j72nlcdfH0wrB2mD2gLkRF1JWoOlA80izGGpMfDJgd0cOI5GkLUUT7QrIorFi0sqGFBtMtTHXHdvn0bX3zxBWbPno13330X69atw507d546iA0bNsDd3R0mJibw8/PDuXPnalx2x44dEAgEai8TExO1ZRhjWLJkCZydnWFqaoqAgADcvn37qeMjuuHsnWzM3RMLJQNe6dkKswd7UaNCAygfaMa5pIe4KVN11qR7qom2onygGVzDgq5YEC3T4KOu0NBQdOzYEQsWLMBvv/2Gffv24cMPP4S3tze++OKLBgewd+9eBAcHY+nSpbh48SJ8fHwQGBiIjIyMGj9jZWWFtLQ07pWcnKw2f82aNfj666+xadMmREdHw9zcHIGBgSgpKWlwfER3fPFPPADgBR8XhI6lDoKaQPlAc/65ng4A6OhshZ7udjxHQ0hVlA80hxoWRGuxBjh27BgTCoVs6dKl7OHDh9z07OxstnjxYmZkZMROnDjRkFWy3r17s1mzZnHvFQoFc3FxYaGhodUuv337dmZtbV3j+pRKJZNKpezzzz/npuXk5DCJRMJ2795dr5hyc3MZAJabm1u/QvCsrKyMHTx4kJWVlfEdSqM0phylcgVrE/IXc1vwJ0vJLmyG6OqPz7+HJrddygdNp65tpqxcwTotCWNuC/5kXx291WxxNCV9yUuV6WKZNJUTKB9o1oyfLzC3BX+y7afu1Gt5Xdx2NcWQ6kYT22+D+lhs2rQJb731FpYtW6Y23c7ODitWrIBMJsPGjRvRv3//eq2vrKwMMTExCAkJ4aYJhUIEBAQgKiqqxs8VFBTAzc0NSqUS3bt3x6effopOnToBUHUgk8lkCAgI4Ja3traGn58foqKiMH78+CrrKy0tRWlpKfc+Ly8PACCXyyGXy+tVFj5VxKgLsdamMeWIl+VDoWQwFxvBycKY17rg8++hye+kfNB06tpmissUKCgtBwC84ddKJ/Z1fclLlelimTQVK+UDzaq4YmFjWr/fO13cdjXFkOpGE2VsUMPi3Llz+Omnn2qc/8Ybb2DixIn1Xl9WVhYUCgWcnNQ7Ijo5OeHmzZvVfqZ9+/bYtm0bunbtitzcXHzxxRfo27cvrl+/jlatWnFjaFe3zop5TwoNDcXy5curTD9y5AjMzMzqXR6+hYeH8x1Ck3iacuy4JQQgRAtxOf7++++mD+op8PH3KCoq0th3UT5oejVtM2UKoCJdhx85AokOjZysL3mpMl0qk6ZyAuUDzUpNNwIgwK1rlyBIrf9TznVp29U0Q6gbTeSDBjUs0tPT4e7uXuN8Dw+PGnfOpuLv7w9/f3/ufd++fdGhQwd89913WLly5VOtMyQkBMHBwdz7vLw8uLq6YtiwYbCysmp0zM1NLpcjPDwcQ4cOhUiku0NQPm05MvNLMTfqBAAg+DkfDO8sba4Q64XPv0fF2TRNoHzQdOraZsJvZADnYiEQAMODAmEi0v6Whb7kpcp0sUyaygmUDzQr9PoJoLgUAf37oktL6zqX18VtV1MMqW40kQ8a1LAoKSmBWCyucb5IJEJZWVm91+fg4AAjIyOkp6erTU9PT4dUWr+DQ5FIhG7duiEhIQEAuM+lp6fD2fm/J9Omp6fD19e32nVIJBJIJFU7QIlEIp3ayHQt3po0tBwHr6g653lLLTHSp5XWPCyIj7+HJr+P8kHTq+k79saonrY9uL0jLM1MqszXZvqSlyrTpTJpKk7KB5pVqLqMCWtzkwbFwXfc2swQ6kYT5Wvwk7e3bNkCCwuLaufl5+c3aF1isRg9evRAREQERo8eDQBQKpWIiIjA7Nmz67UOhUKBq1evYsSIEQBUZ0WkUikiIiK4RJGXl4fo6GjMmDGjQfER3XAmIRsA8Jpfa61pVBgKygfN735OMU7cygQAvNijFc/REFIzygeaUSJXoPBxnytLkwYfxhHSrBq0RbZu3RqbN2+uc5mGCA4OxqRJk9CzZ0/07t0b69evR2FhIaZMmQIAmDhxIlq2bInQ0FAAwIoVK9CnTx94enoiJycHn3/+OZKTk/HWW28BAAQCAebNm4dVq1bBy8sLHh4eWLx4MVxcXLjkRPTLg9xiAICnI43tr0mUDzRj1Z83AAAOFmIM9nbkORpCqkf5QHP+vJIGJQNa2pjSA/KI1mlQw+Lu3btNHsC4ceOQmZmJJUuWQCaTwdfXF2FhYVznqpSUFAiF/z1u49GjR5g2bRpkMhlsbW3Ro0cPnDlzBh07duSWmT9/PgoLC/H2228jJycHzzzzDMLCwqo8KIfovuyCUqQ+VHVGamljynM0hoXyQfMrKC3HP9dV96WvHNVZJ/pWEMNE+UBzkrMLAQCDvFtAIKCr9ETLNGRs2oiICNahQ4dqx7/NyclhHTt2ZP/++2+TjYXLF20Zp7q+9GUM5qcpx8bIBOa24E/2/DcnmzGyhjGU51hQPmg6NW0zDwtKmduCP5nbgj+ZQqFstu9vDvqSlyrTxTJpKidQPtCchb9dZm4L/mRfhsfX+zO6uO1qiiHVjSa23wY9eXv9+vWYNm1atSMhWFtbY/r06Vi3bl0TNHcIqZ9fzqcCAF73c+M5EsND+aD5XX+guVG+CGkMygeaEy9T9VfxcDDnORJCqmpQw+Ly5csICgqqcf6wYcMQExPT6KAIqY+8EjnuZKkuCQfyPMSsIaJ80Py2n04CAPT2sKOBCYhWo3ygGXklclxMyQEAtJdSv0KifRrUsEhPT691qCpjY2NkZmY2OihC6uP07SwAgIu1CaxN9XuIOG1E+aB5PSws40aDer0PXZEj2o3ygWasD78NALAxE8HNjq5YEO3ToIZFy5Ytce3atRrnX7lyRW1saEKay99X0zBz10UAQD9PB56jMUyUD5rX39fSUK5kMBUZYVD7FnyHQ0itKB9oxu0M1W1Q0/u3hamYBnMg2qdBDYsRI0Zg8eLFKCkpqTKvuLgYS5cuxciRI5ssOEKqE30nG3P2XAJjQJ82dvj4uQ58h2SQKB80r7JyJQBgcAdHWJrQFTmi3SgfaEbB4+dXtGlBVyuIdmrQcLOLFi3C/v370a5dO8yePRvt27cHANy8eRMbNmyAQqHAxx9/3CyBElLh5+gUyBUMre3MsHVSL5hL6AFBfKB80LxKHzcshDScJNEBlA+an0LJkJBeAACQWunO8LjEsDToiMzJyQlnzpzBjBkzEBISAsYYANVDZwIDA7FhwwZufGlCmkNhaTkup+YAAN4f1o4aFTyifNB8lEqGXdEpAAArerIu0QGUD5pfTlEZ8h9fsejkUnX0LUK0QYN/sdzc3HD48GE8evQICQkJYIzBy8sLtra2zREfIZyyciXm/3YFKQ+LYGcuxsD29BRivlE+aB7p+SVIefzgx+n92/IcDSH1Q/mgeSU9HgXRyUoCY6MG3clOiMY89akwW1tb9OrVqyljIaRGNx7kIfiXWNx8PH73+nG+NBKUFqF80DzERkK0tjfjOwxCGoTyQfO4fC8XANClpQ2/gRBSC2ryEp2w5NA13JTlw85cjK8ndEP/djRKDiGEEMMRl6Z6YGaXltY8R0JIzejmXaL15Aoldwl4w6vd4d/WnueICGleCiXjOwRCiJbJLZYDAOwtxDxHQkjN6IoF0Xpzdl9CdmEZTERCdGpJHdaI/tt6SvXEbRszut2PEKJSVKbquG1Bg5YQLUYNC6LV/rqShr+vySAQAF+P7wYrGs+fGICLyY8AABN6t+Y5EkKItigoVQAAzOjBeESLUbOXaKXUh0VYf/Q2frt4DwDwSg9XDOsk5TkqQjSrayu6l5oQohoVMSFdNXhJC0sJz9EQUjNqWBCt882xRHwTmQjGAKEAmPqMB4KHtuc7LEI0QqFkuPeomO8wCCFaJKugFIVlChgLBfBpZcN3OITUiBoWRKtczBLgh9uJAIDeHnZ4f2g7+LWhztrEcBy+mobswjIAQNsWFjxHQwjRBo+KVDnBxkwMoVDAczSE1IwaFkRrlJYrcShZ1e3nua7O2PBqd54jIkTzKq5WDGrfAu4O5jxHQwjRBjlFqhGh7MypnyHRbtR5m2iFv66koe9nkcgpU52JCR3bheeICOFHRn4JAMDBgu6jJoSoVL5iQYg2oysWhHclcgUWH7qGvJJy2IgZ5gztQKM/EYNUWFqOX86nAgBc7eiJ24QQlewCVcPCloagJlqOGhaEF3klchyLy8CF5Ic4fjMTDwvLILWSYH6HQjzfh4bYJIbpYmoOCstUQ0pOe7YNz9EQQrTFTZnqqdvU74poO2pYEI1RKhnOJmVj34V7OHw1DaXlSm6exFiIVaM6ojDhPI8REsKv7aeTAQA93GxhSmPVE0Iey8wvBQC0tDXlORJCaqcVfSw2bNgAd3d3mJiYwM/PD+fOnatx2c2bN+PZZ5+Fra0tbG1tERAQUGX5yZMnQyAQqL2CgoKauxikFpn5pXj7pxi8ujkaBy7dR2m5Ep6OFnjrGQ9sfK07Ti8cjAHtWvAdJtEChpwP0vNUBw9T+rnzGwghWsKQ80FlFQ2LFtT3img53hsWe/fuRXBwMJYuXYqLFy/Cx8cHgYGByMjIqHb5yMhITJgwAcePH0dUVBRcXV0xbNgw3L9/X225oKAgpKWlca/du3drojjkCQ8LyzDj5xj4fXoUR+PSAQDje7ni4Kx+CH+vPxaN7IjhXZypoyoBYNj5QMmA9Mcdt22pgyYhBp0PnvTwcedtK1PqY0G0G+8Ni3Xr1mHatGmYMmUKOnbsiE2bNsHMzAzbtm2rdvmdO3di5syZ8PX1hbe3N7Zs2QKlUomIiAi15SQSCaRSKfeytbXVRHEIVA/4SsgowMFL9zFkbST+viaDkgE+rjb4/o0eWP1iV/i62kAgoLG4iTpDzgcxWQLkFpcDAFpTx21CDDofVFZarkDqQ9Uw1G1a0BDURLvx2seirKwMMTExCAkJ4aYJhUIEBAQgKiqqXusoKiqCXC6HnZ2d2vTIyEg4OjrC1tYWgwcPxqpVq2BvX/2D1kpLS1FaWsq9z8tTdZKSy+WQy+UNLZbGVcSoDbFm5pdizKaz3C0dACA2FmL31F7o2soaQM1xalM5GoPPcuhy3RlyPpDL5cgsUTW0h3i3gNRSpNN/S0B/9ufKdLFMuhRrZYacD550/X4uANWIUDYS4VN9ry5uu5piSHWjiTLy2rDIysqCQqGAk5OT2nQnJyfcvHmzXutYsGABXFxcEBAQwE0LCgrC2LFj4eHhgcTERHz00UcYPnw4oqKiYGRUtUNkaGgoli9fXmX6kSNHYGamO2cOw8PDef1+xoBv44RIz1NdCPOwZHA1Z/CxL8e9K6dx70r91sN3OZoKH+UoKirS+Hc2FUPOB4wBl7JUsZTnpuPw4cPN8j180Jf9uTJdKpOu5gRDzgdPinskAGAEM5Th77//btS6dGnb1TRDqBtN5AOdHhVq9erV2LNnDyIjI2FiYsJNHz9+PPf/Ll26oGvXrmjbti0iIyMxZMiQKusJCQlBcHAw9z4vL4+7N9PKyqp5C9EE5HI5wsPDMXToUIhE/N1/eeVeLm6djQYA/DC5B/q2rf4MUE20pRyNxWc5Ks6mGSJdzgc37j9CxlnViGgfju2H9lLLZvkeTdKX/bkyXSyToeYEXc4HT5JfTgNuXoWb1B4jRvR8unXo4LarKYZUN5rIB7w2LBwcHGBkZIT09HS16enp6ZBKpbV+9osvvsDq1atx9OhRdO3atdZl27RpAwcHByQkJFSbOCQSCSSSqp2HRSKRTm1kfMe767yqg1xQJykGeNf+96sN3+VoKnyUQ5frzZDzwZ6YNACAm50ZOrva1bG0btGX/bkyXSqTrsT5JEPOB0/KL1U928bWXNLo79SlbVfTDKFuNFE+Xjtvi8Vi9OjRQ61jVUVHK39//xo/t2bNGqxcuRJhYWHo2bPu1vu9e/eQnZ0NZ2fnJomb/Ke4TIF/b2UiZP9V/HbxHgBgdLeWPEdFdJEh54PwONUoNy/3oH2HEMCw88GTcopU98Xb0FO3iQ7g/Vao4OBgTJo0CT179kTv3r2xfv16FBYWYsqUKQCAiRMnomXLlggNDQUAfPbZZ1iyZAl27doFd3d3yGQyAICFhQUsLCxQUFCA5cuX48UXX4RUKkViYiLmz58PT09PBAYG8lZOfSNXKHHkejo+PniVS3oA8F5AOwR2cqrlk4TUzBDzwdEb6cgqUA0lGdjJkedoCNEehpgPqpPzeKhZalgQXcB7w2LcuHHIzMzEkiVLIJPJ4Ovri7CwMK7DVkpKCoTC/y6sbNy4EWVlZXjppZfU1rN06VIsW7YMRkZGuHLlCn744Qfk5OTAxcUFw4YNw8qVK6u9nEkapkSuwBf/xGPv+VTkl6qGxrQzF2OwtyOGd5ZiSAdqVJCnZ4j54FRCFgCgnbUS7vY0lCQhFQwxH1Tnbraqw62zNT11m2g/3hsWADB79mzMnj272nmRkZFq7+/evVvrukxNTfHPP/80UWSkQlm5Elfv52DZ7zdw9fHQd/bmYgzvIkXI8A4wl2jFpkT0gCHlg7+vpuHns8kAgK52jOdoCNE+hpQPanI/5/EzLBzoxAPRfnQ0SKooKivHsZsZuJ1egDtZhUjMKEBiZgFKy5XcMt9M6IbnujhDKKSH3BHytD7/Jx7lSoaRXaTwN7vHdziEEC2TU1SGlMdXLOip20QXUMOCcK4/yMXhq2nYfS4VDwvLqsy3Mxejt7sdxvV2xaD2dC84IY1VWKa6nfCtZ9yRHEsNC0KIukupOShTKOFqZ4qOzto//D0h1LAgAIDPwm5iY2Qi997VzhR92zigTQtztG1hgbaOFnC3N4NAQFcoCGkK2QWlak+oJ4SQJ127p7r1uLOLNd0hQHQCNSwMXF6JHAt+vYK/r6lGz3jWywGv9HTF8M5SGBvxOhoxIXptTVg8AMDKxBhtW5gjmed4CCHaJz49HwDQyYWuVhDdQA0LA1VQWo4Ldx9i2e/XcTe7CMZCAeYM8cKcIV58h0aI3rv3qAh7L6QCAOYHecNEZMRzRIQQbXQjTfWk5E4u1jxHQkj9UMPCAP1yPhWLDl5DmULVGVtsJMSe6X3QvbUtz5ERYhhSH6pGebE0McarvVtDoSjnOSJCiLaJvpONO5mFAIBWtjTULNEN1LAwICnZRfg2MgF7zqvOlDpZSTCgXQuM792aGhWEaFDFA6+kViYQCgVQKHgOiBCidY7cSAegukXZy8mS52gIqR9qWBiIE7cyMf2nCyiRq65STPR3w/IXOlFnbEJ48N2/dwAAUmsTniMhhGgjxhiOxqkaFuN6ufIcDSH1Rw0LPXfvURGWHrqOiJsZAFRnSL+e0A29Pex4jowQw3Q8PgOxqTkAgHcHU58mQkhVSVmFSM4ugthYSMO7E51CDQs9lpRViEFfRHLvh3Z0wiejO8PRis6SEsKX/RfvAwCe8XSgBj4hpFpX76uGme3obAVzCR2qEd1BW6ueYYwhKasQm0/ewe5zqdz0X6b700EMITzLyCvBX1ceAACe6+rMczSEEG0VGZ8JAPS7TXQONSz0RFE5sD4iAQcuPcCD3BJuev92LTC5rxslJ0K0wMq/4qBkgK2ZCGO6teQ7HEKIFmKMISoxGwAwsF0LnqMhpGGoYaHjbsry8OWReETEGaGcqTqEio2E6NbaBu8MbEv3ZhKiRSIed8ZcOJyeXUEIqd4/12WQ5ZXARCSEj6sN3+EQ0iDUsNBhN2V5ePHbMygsUwAQwFtqiXcHe2GwtyNMxXTQQog2ySuRo6hMNa5sP08HnqMhhGgjhZLhiyO3AABv9HGj/hVE59AWq6Nupedj/q9XUFimQPfWNhhik4VpL/WBWCzmOzRCSDU++/smAMDGTAQHCwnP0RBCtNHphCwkZBTASCjA9AFt+Q6HkAYT8h0AabhvIm5j2Jf/4sq9XJiJjbD+la5oZQ56JgUhWir1YRF2RqcAAOYN8aLboAgh1frkrzgAwCs9W9EJCKKTqGGhY67cy8HacNVl0mEdnfDLdH8400O2CNFqFQ+6sjMX47U+bjxHQwjRRrlFcsSn5wMAPgz05jkaQp4O3QqlpRRKhgc5xUh5WITk7CIkPyzExeRHuJD8CADQycUK30/sCQCQy+V8hkoIqcOOM3cBqEZ4ERnR+RxCSFVpecUAAFOREezM6bZmopuoYaGFou9k480d5x93yq6qo7MVvp7QTcNREUKeVnJ2EQBg5iBPniMhhGirn6KSAQC9aHh4osOoYaFlFEqGTw7HcY2KNi3M4WZnBjd7c7RzssSzXg5wtTPjOUpCSH08LCzDOz/HcO/t6SwkIaQaGXkl2HfhHgBg5kDqtE10FzUstMyX4bdw5V4uAHpaNiG67uuI2ziX9BAA8NYzHrClhgUhpBLGGO5mF2HtkXiUKZTo6GwFP/rdJzqMGhZaoFyhxPUHeQi7LsP3/6oecje9fxtqVBCiw+7nFHN9K+YM9kTwsPb8BkQI0RqFpeX4+5oM/zt2G3cf3yoJAON6udIIj0SnaUUvwg0bNsDd3R0mJibw8/PDuXPnal1+37598Pb2homJCbp06YLDhw+rzWeMYcmSJXB2doapqSkCAgJw+/bt5ixCjcrKlZDlluDGgzycup2FQ7H3sf10EtYdicfHB67ija3R8Fl+BKM2nMbGyEQolAzdW9vgg0A6CCGGSdfzQWR8Bl7edAbPfHYMAOBgIcbcgHbN9n2E6DNdzwdP2nMuBaP+dwpdlx/BB/su4252EURGAvRpY4c1L3bFRH8aNY7oNt6vWOzduxfBwcHYtGkT/Pz8sH79egQGBiI+Ph6Ojo5Vlj9z5gwmTJiA0NBQjBw5Ert27cLo0aNx8eJFdO7cGQCwZs0afP311/jhhx/g4eGBxYsXIzAwEDdu3ICJSdMNzVquUCIttwSpD4tw71ExHuQWIz2vBLLcEsjySpGeV4KHhWX1WpeliTH6tXXAc12dMbSjE40cQwySLueDBznF+OtKGkL/joOSqaZ1bWWNz17sCiMhnYEkpKF0OR8AqkZMbrEcWQWlyMwvw52sAnx84Bo3v6WNKV7r0xqT/N3pCdtEbwgYY4zPAPz8/NCrVy/873//AwAolUq4urri3XffxcKFC6ssP27cOBQWFuLPP//kpvXp0we+vr7YtGkTGGNwcXHB+++/jw8++AAAkJubCycnJ+zYsQPjx4+vM6a8vDxYW1sjNzcXVlZWavOyCkoxZ/clpD4qwoOcEiiUdVefkVAAWzMx7MxFsDMXw95cAjtzMWzNxXCykqCbqy3aSy2f+uBDLpfj8OHDGDFiBEQi0VOtQxtQORqvtm1XF+haPmCMIfiXyzh7JxtpuSXc9Jd6tMJ7Q9uhpY1pvcqtL9t+ZVQm7aDLOUHX8gEAbDiegMNX05BVUIrsgjKUV3OMYG0qwuG5z9Y7PzQFXdx2NcWQ6kYT+YDXJnJZWRliYmIQEhLCTRMKhQgICEBUVFS1n4mKikJwcLDatMDAQBw8eBAAkJSUBJlMhoCAAG6+tbU1/Pz8EBUVVW3iKC0tRWlpKfc+Ly8PgGpje/IZESZChjOJ2dx7kZEArramaGVrCmdrEzhZmsDJSvL4ZQJHSwlsTEUQ1tFoUCrKoax+dNk6VcSo68+zoHI03XfrIl3MBwCQmJGPtNwSGAkF6CC1xPNdpZjS1w0CgaDefw992fYrozJpB12KtTJdzQeynCJcf5CnNs3KxBgOFmLYW0jgbm+GOYPbwtHcWKN/G13cdjXFkOpGE2XktWGRlZUFhUIBJycntelOTk64efNmtZ+RyWTVLi+Tybj5FdNqWuZJoaGhWL58eZXpR44cgZlZ1aFdJ3sJYCVmsJcAVmJAKJADeJxISlSvwgzgDlQvTQkPD9fgtzUfKsfTKyoqqnshLaWr+aCflQD9rYHWFgwSo4dA7kP8/feNGkpZO33Z9iujMvFLV3OCruYD52LgbW8BrEQMFiLAUgQYC8uhOjhQuXgqudrv0gRd2nY1zRDqRhP5gG7qAxASEqJ2liMvLw+urq4YNmxYtZeKRmgyuHqQy+UIDw/H0KFDdfoyHpWj8SrOppGnx0c+0JdtvzIqk3agnNA4Dc0H2koXt11NMaS60UQ+4LVh4eDgACMjI6Snp6tNT09Ph1QqrfYzUqm01uUr/k1PT4ezs7PaMr6+vtWuUyKRQCKRVJkuEol0aiPTtXhrQuVo3HfqKsoH+rPtV0Zl4peuxPkkygfNQ1fj1gRDqBtNlI/XoYfEYjF69OiBiIgIbppSqURERAT8/f2r/Yy/v7/a8oDq8lXF8h4eHpBKpWrL5OXlITo6usZ1EkL4R/mAEFKB8gEhuon3W6GCg4MxadIk9OzZE71798b69etRWFiIKVOmAAAmTpyIli1bIjQ0FAAwd+5cDBgwAGvXrsVzzz2HPXv24MKFC/j+++8BAAKBAPPmzcOqVavg5eXFDSfn4uKC0aNH81VMQkg9UD4ghFSgfECI7uG9YTFu3DhkZmZiyZIlkMlk8PX1RVhYGNe5KiUlBULhfxdW+vbti127dmHRokX46KOP4OXlhYMHD3JjVAPA/PnzUVhYiLfffhs5OTl45plnEBYWVu8xqitG4NWVe1PlcjmKioqQl5en05fxqByNV7HN8jyK9FMz1HygL9t+ZVQm7aDLOcFQ80Fz0MVtV1MMqW40kQ94f46FNrp37x5cXV35DoOQp5aamopWrVrxHYZeoHxA9AHlhKZB+YDog+bMB9SwqIZSqcSDBw9gaWkJgUD7n5hbMUpFamqqTo1S8SQqR+MxxpCfnw8XFxe1M3nk6WkiH+jLtl8ZlUk7UE5oWrp2fFBBF7ddTTGkutFEPuD9VihtJBQKdfLMjpWVlV7sFFSOxrG2ttb4d+ozTeYDfdn2K6My8Y9yQtPR1eODCrq27WqSodRNc+cDOn1BCCGEEEIIaTRqWBBCCCGEEEIajRoWekAikWDp0qXVPsRHl1A5iKHSx22GykSI9qBtt2ZUN02LOm8TQgghhBBCGo2uWBBCCCGEEEIajRoWhBBCCCGEkEajhgUhhBBCCCGk0ahhQQghhBBCCGk0aljoCHd3dwgEgiqvWbNmAQAGDhxYZd4777zDc9TAv//+i+effx4uLi4QCAQ4ePCg2nzGGJYsWQJnZ2eYmpoiICAAt2/fVlvm4cOHeO2112BlZQUbGxtMnToVBQUFGixF7eWQy+VYsGABunTpAnNzc7i4uGDixIl48OCB2jqq+xuuXr1ao+Ug/GqK/UHbhIaGolevXrC0tISjoyNGjx6N+Ph4tWVKSkowa9Ys2Nvbw8LCAi+++CLS09N5irhuGzduRNeuXbkHZvn7++Pvv//m5utaeYhh2LBhA9zd3WFiYgI/Pz+cO3euxmU3b96MZ599Fra2trC1tUVAQECty+u6htRNZXv27IFAIMDo0aObN0A9Qg0LHXH+/HmkpaVxr/DwcADAyy+/zC0zbdo0tWXWrFnDV7icwsJC+Pj4YMOGDdXOX7NmDb7++mts2rQJ0dHRMDc3R2BgIEpKSrhlXnvtNVy/fh3h4eH4888/8e+//+Ltt9/WVBEA1F6OoqIiXLx4EYsXL8bFixexf/9+xMfH44UXXqiy7IoVK9T+Ru+++64mwidaoin2B21z4sQJzJo1C2fPnkV4eDjkcjmGDRuGwsJCbpn33nsPf/zxB/bt24cTJ07gwYMHGDt2LI9R165Vq1ZYvXo1YmJicOHCBQwePBijRo3C9evXAeheeYj+27t3L4KDg7F06VJcvHgRPj4+CAwMREZGRrXLR0ZGYsKECTh+/DiioqLg6uqKYcOG4f79+xqOvPk1tG4q3L17Fx988AGeffZZDUWqJxjRSXPnzmVt27ZlSqWSMcbYgAED2Ny5c/kNqg4A2IEDB7j3SqWSSaVS9vnnn3PTcnJymEQiYbt372aMMXbjxg0GgJ0/f55b5u+//2YCgYDdv39fY7FX9mQ5qnPu3DkGgCUnJ3PT3Nzc2Jdfftm8wRGd8TT7gy7IyMhgANiJEycYY6oyiEQitm/fPm6ZuLg4BoBFRUXxFWaD2drasi1btuhNeYh+6d27N5s1axb3XqFQMBcXFxYaGlqvz5eXlzNLS0v2ww8/NFeIvHmauikvL2d9+/ZlW7ZsYZMmTWKjRo3SQKT6ga5Y6KCysjL8/PPPePPNNyEQCLjpO3fuhIODAzp37oyQkBAUFRXxGGXdkpKSIJPJEBAQwE2ztraGn58foqKiAABRUVGwsbFBz549uWUCAgIgFAoRHR2t8ZjrKzc3FwKBADY2NmrTV69eDXt7e3Tr1g2ff/45ysvL+QmQaJ367A+6IIy061cAABleSURBVDc3FwBgZ2cHAIiJiYFcLlcrl7e3N1q3bq0T5VIoFNizZw8KCwvh7++v8+Uh+qesrAwxMTFq26RQKERAQEC9t8mioiLI5XJuv9UXT1s3K1asgKOjI6ZOnaqJMPWKMd8BkIY7ePAgcnJyMHnyZG7aq6++Cjc3N7i4uODKlStYsGAB4uPjsX//fv4CrYNMJgMAODk5qU13cnLi5slkMjg6OqrNNzY2hp2dHbeMtikpKcGCBQswYcIEWFlZcdPnzJmD7t27w87ODmfOnEFISAjS0tKwbt06HqMl2qI++4O2UyqVmDdvHvr164fOnTsDUJVLLBZXaWRre7muXr0Kf39/lJSUwMLCAgcOHEDHjh0RGxurk+Uh+isrKwsKhaLa3HHz5s16rWPBggVwcXFROwDXB09TN6dOncLWrVsRGxurgQj1DzUsdNDWrVsxfPhwuLi4cNMq9zno0qULnJ2dMWTIECQmJqJt27Z8hGmQ5HI5XnnlFTDGsHHjRrV5wcHB3P+7du0KsViM6dOnIzQ0FBKJRNOhEtLkZs2ahWvXruHUqVN8h9Jo7du3R2xsLHJzc/Hrr79i0qRJOHHiBN9hEdLkVq9ejT179iAyMhImJiZ8h8Or/Px8vPHGG9i8eTMcHBz4DkcnUcNCxyQnJ+Po0aN1Xonw8/MDACQkJGhtw0IqlQIA0tPT4ezszE1PT0+Hr68vt8yTHazKy8vx8OFD7vPaoqJRkZycjGPHjqldraiOn58fysvLcffuXbRv315DURJtVZ/9QZvNnj2bG1yhVatW3HSpVIqysjLk5OSoneVPT0/Xun24MrFYDE9PTwBAjx49cP78eXz11VcYN26cTpaH6C8HBwcYGRlVGZmsPtvkF198gdWrV+Po0aPo2rVrc4bJi4bWTWJiIu7evYvnn3+em6ZUKgGo7paIj4/X2mMqbUF9LHTM9u3b4ejoiOeee67W5Sou4VU+QNE2Hh4ekEqliIiI4Kbl5eUhOjoa/v7+AAB/f3/k5OQgJiaGW+bYsWNQKpVc40kbVDQqbt++jaNHj8Le3r7Oz8TGxkIoFFa51YsYpvrsD9qIMYbZs2fjwIEDOHbsGDw8PNTm9+jRAyKRSK1c8fHxSElJ0epyPUmpVKK0tFRvykP0h1gsRo8ePdS2SaVSiYiIiFq3yTVr1mDlypUICwtT68eoTxpaN97e3rh69SpiY2O51wsvvIBBgwYhNjYWrq6umgxfN/Hde5zUn0KhYK1bt2YLFixQm56QkMBWrFjBLly4wJKSktihQ4dYmzZtWP/+/XmK9D/5+fns0qVL7NKlSwwAW7duHbt06RI3WtLq1auZjY0NO3ToELty5QobNWoU8/DwYMXFxdw6goKCWLdu3Vh0dDQ7deoU8/LyYhMmTNCacpSVlbEXXniBtWrVisXGxrK0tDTuVVpayhhj7MyZM+zLL79ksbGxLDExkf3888+sRYsWbOLEiRotB+FXU+wP2mbGjBnM2tqaRUZGqm37RUVF3DLvvPMOa926NTt27Bi7cOEC8/f3Z/7+/jxGXbuFCxeyEydOsKSkJHblyhW2cOFCJhAI2JEjRxhjulceov/27NnDJBIJ27FjB7tx4wZ7++23mY2NDZPJZIwxxt544w22cOFCbvnVq1czsVjMfv31V7X9Nj8/n68iNJuG1s2TaFSohqGGhQ75559/GAAWHx+vNj0lJYX179+f2dnZMYlEwjw9PdmHH37IcnNzeYr0P8ePH2cAqrwmTZrEGFMNsbl48WLm5OTEJBIJGzJkSJXyZWdnswkTJjALCwtmZWXFpkyZovHkV1s5kpKSqp0HgB0/fpwxxlhMTAzz8/Nj1tbWzMTEhHXo0IF9+umnrKSkRKPlIPxqiv1B29S07W/fvp1bpri4mM2cOZPZ2toyMzMzNmbMGJaWlsZf0HV48803mZubGxOLxaxFixZsyJAhXKOCMd0rDzEM33zzDWvdujUTi8Wsd+/e7OzZs9y8AQMGcHmGMdXw59Xtt0uXLtV84BrQkLp5EjUsGkbAGGPNe02EEEIIIYQQou+ojwUhhBBCCCGk0ahhQQghhBBCCGk0algQQgghhBBCGo0aFoQQQgghhJBGo4YFIYQQQgghpNGoYUEIIYQQQghpNGpYEEIIIYQQQhqNGhaEEEIIIYSQRqOGBamRu7s71q9fX+P8u3fvQiAQIDY2VmMxPfndAoEAvr6+tS47efJkjB49usHfUbF+Gxubp4qREE0RCAQ4ePAgAH73y6awbNmyOvdpXRQZGQmBQICcnBy+QyHEIA0cOBDz5s3jOwy9Rw0LPTB58mTuIFgsFsPT0xMrVqxAeXl5vT6/Y8cOnT14Pnr0KCIiIhr0mcr1JRAIYG9vj6CgIFy5ckVtubS0tFobVoQ0VGZmJsRiMQoLCyGXy2Fubo6UlBS+w2o0TTYGKp9UEAgEsLOzw4ABA3Dy5MkqMVVeruLl7e3dqO8/ceIEXF1dG7WO+qrr5A4hzaGuY4qKRvKTr0WLFmk81sonVeqyf/9+rFy5snkDIjDmOwDSNIKCgrB9+3aUlpbi8OHDmDVrFkQiEUJCQvgOrVnZ29vD3t6+wZ+rqC8AkMlkWLRoEUaOHKl2kCeVSmFtbd1ksRISFRUFHx8fmJubIzo6GnZ2dmjdujXfYemko0ePolOnTsjKysInn3yCkSNH4tatW3BycuKW6dSpE44ePar2OWPjxv3sHTp0CM8//3yj1kGItqvPMUV8fDysrKy49xYWFnyEWqeysjKIxWLY2dnxHYpBoCsWekIikUAqlcLNzQ0zZsxAQEAAfv/9dwDAunXr0KVLF5ibm8PV1RUzZ85EQUEBANWZhylTpiA3N5c767Bs2TJuvUVFRXjzzTdhaWmJ1q1b4/vvv681jhMnTqB3796QSCRwdnbGwoUL1a6c/Prrr+jSpQtMTU1hb2+PgIAAFBYWcrH07t0b5ubmsLGxQb9+/ZCcnNygelAoFAgODoaNjQ3s7e0xf/58MMZqrC+pVApfX18sXLgQqampyMzMbND3EdIQZ86cQb9+/QAAp06d4v5fm/Pnz2Po0KFwcHCAtbU1BgwYgIsXLzboe+vaL5VKJdasWQNPT09IJBK0bt0an3zyCTd/wYIFaNeuHczMzNCmTRssXrwYcrkcgOqK5/Lly3H58mUuh+zYsQMAkJOTg7feegstWrSAlZUVBg8ejMuXL6vFtnr1ajg5OcHS0hJTp05FSUlJvcpkb28PqVSKzp0746OPPkJeXh6io6PVljE2Nub284qXg4MDN//bb7+Fl5cXTExM4OTkhJdeeqnO7/3999/xwgsvVDsvOTkZzz//PGxtbWFubo5OnTrh8OHDNa7rt99+Q6dOnSCRSODu7o61a9dy8wYOHIjk5GS89957XL0Soim1HVNUcHR0VNu3amtYCAQCfPfddxg5ciTMzMzQoUMHREVFISEhAQMHDoS5uTn69u2LxMREtc8dOnQI3bt3h4mJCdq0aYPly5dzucvd3R0AMGbMGAgEAu59xRXULVu2wMPDAyYmJgCq3gpVWlqKBQsWwNXVFRKJBJ6enti6dWsja45Qw0JPmZqaoqysDAAgFArx9ddf4/r16/jhhx9w7NgxzJ8/HwDQt29frF+/HlZWVkhLS0NaWho++OADbj1r165Fz549cenSJcycORMzZsxAfHx8td95//59jBgxAr169cLly5exceNGbN26FatWrQKgurVowoQJePPNNxEXF4fIyEiMHTsWjDGUl5dj9OjRGDBgAK5cuYKoqCi8/fbbDf4xXbt2LXbs2IFt27bh1KlTePjwIQ4cOFDrZwoKCvDzzz/D09Pzqa5+EFKblJQU2NjYwMbGBuvWrcN3330HGxsbfPTRRzh48CBsbGwwc+bMGj+fn5+PSZMm4dSpUzh79iy8vLwwYsQI5Ofn1+v769ovASAkJASrV6/G4sWLcePGDezatUvtzL+lpSV27NiBGzdu4KuvvsLmzZvx5ZdfAgDGjRuH999/H506deJyyLhx4wAAL7/8MjIyMvD3338jJiYG3bt3x5AhQ/Dw4UMAwC+//IJly5bh008/xYULF+Ds7Ixvv/22QfVbXFyMH3/8EQAgFovr/bkLFy5gzpw5WLFiBeLj4xEWFob+/fvX+pnr168jIyMDgwcPrnb+rFmzUFpain///RdXr17FZ599VuPBVkxMDF555RWMHz8eV69exbJly7B48WKuUbZ//360atUKK1as4OqVEL5UPqZ4WitXrsTEiRMRGxsLb29vvPrqq5g+fTpCQkJw4cIFMMYwe/ZsbvmTJ09i4sSJmDt3Lm7cuIHvvvsOO3bs4E56nD9/HgCwfft2pKWlce8BICEhAb/99hv2799fY3+ziRMnYvfu3fj6668RFxeH7777TmuvuugURnTepEmT2KhRoxhjjCmVShYeHs4kEgn74IMPql1+3759zN7ennu/fft2Zm1tXWU5Nzc39vrrr3PvlUolc3R0ZBs3bmSMMZaUlMQAsEuXLjHGGPvoo49Y+/btmVKp5D6zYcMGZmFhwRQKBYuJiWEA2N27d6t8V3Z2NgPAIiMj61XmJ7+7grOzM1uzZg33Xi6Xs1atWnH1w5iqvoyMjJi5uTkzNzdnAJizszOLiYmp8j011Q0h9SWXy1lSUhK7fPkyE4lE7PLlyywhIYFZWFiwEydOsKSkJJaZmVnv9SkUCmZpacn++OMPbhoAduDAAcZYw/fLvLw8JpFI2ObNm+sdw+eff8569OjBvV+6dCnz8fFRW+bkyZPMysqKlZSUqE1v27Yt++677xhjjPn7+7OZM2eqzffz86uyrsoqymdqasrMzc2ZQCBgAFiPHj1YWVmZWkxCoZDbzyte06dPZ4wx9ttvvzErKyuWl5dX73J/8skn7KWXXqpxfpcuXdiyZcuqnXf8+HEGgD169Igxxtirr77Khg4dqrbMhx9+yDp27Mi9d3NzY19++WW94yOkKdR1TFGxLT+5b2VlZdW4TgBs0aJF3PuoqCgGgG3dupWbtnv3bmZiYsK9HzJkCPv000/V1vPTTz8xZ2dntfVW5L4KS5cuZSKRiGVkZKhNHzBgAJs7dy5jjLH4+HgGgIWHh9ddIaRBqI+Fnvjzzz9hYWEBuVwOpVKJV199lbul6ejRowgNDcXNmzeRl5eH8vJylJSUoKioCGZmZrWut2vXrtz/BQIBpFIpMjIyql02Li4O/v7+alcZ+vXrh4KCAty7dw8+Pj4YMmQIunTpgsDAQAwbNgwvvfQSbG1tYWdnh8mTJyMwMBBDhw5FQEAAXnnlFTg7O9e7DnJzc5GWlgY/Pz9umrGxMXr27FnldqhBgwZh48aNAIBHjx7h22+/xfDhw3Hu3Dm4ubnV+zsJqYuxsTHc3d3xyy+/oFevXujatStOnz4NJyenOs+QA0B6ejoWLVqEyMhIZGRkQKFQoKioqN6dvuvaL2UyGUpLSzFkyJAa17F37158/fXXSExMREFBAcrLy9Xura7O5cuXUVBQUOUqYHFxMXe7Q1xcHN555x21+f7+/jh+/Hid5dq7dy+8vb1x7do1zJ8/Hzt27IBIJFJbpn379lVu36iIe+jQoXBzc0ObNm0QFBSEoKAgjBkzptaceOjQIbUzqk+aM2cOZsyYgSNHjiAgIAAvvviiWg6tLC4uDqNGjVKb1q9fP6xfvx4KhQJGRka1lp+Q5lTbMUWFkydPwtLSkntva2tb6zor7wsVV0S7dOmiNq2kpAR5eXmwsrLC5cuXcfr0abXbMhUKRb2OX9zc3NCiRYsa58fGxsLIyAgDBgyoNWbScNSw0BMVB8pisRguLi5cB8W7d+9i5MiRmDFjBj755BPY2dnh1KlTmDp1KsrKyupsWDz5Qy0QCKBUKp8qRiMjI4SHh+PMmTM4cuQIvvnmG3z88ceIjo6Gh4cHtm/fjjlz5iAsLAx79+7FokWLEB4ejj59+jzV99XG3Nwcnp6e3PstW7bA2toamzdvVrtFhJDG6tSpE5KTk7kfaAsLC5SXl6O8vBwWFhZwc3PD9evXa/z8pEmTkJ2dja+++gpubm6QSCTw9/dv9G0JFUxNTWudHxUVhddeew3Lly9HYGAgrK2tsWfPHrX+ANUpKCiAs7MzIiMjq8xrilHoXF1d4eXlBS8vL5SXl2PMmDG4du0aJBIJt0zFiDbVsbS0xMWLFxEZGYkjR45gyZIlWLZsGc6fP19tfGlpabh06RKee+65GmN66623EBgYiL/++gtHjhxBaGgo1q5di3fffbfR5SVEk2o6pqjMw8OjQfty5eOJihMd1U2rOMYoKCjA8uXLMXbs2Crrqug3URNzc/Na59eV98jToz4WeqLiQLl169ZqCSAmJgZKpRJr165Fnz590K5dOzx48EDts2KxGAqFotExVHTGqnx14PTp07C0tESrVq0AqBJHv379sHz5cly6dAlisVitD0S3bt0QEhKCM2fOoHPnzti1a1e9v9/a2hrOzs5qHTjLy8sRExNT52cFAgGEQiGKi4vr/X2E1Mfhw4cRGxsLqVSKn3/+GbGxsejcuTPWr1+P2NjYWjv3Aqp9aM6cORgxYgTX0TcrK6ve31/Xfunl5QVTU9Mah20+c+YM3Nzc8PHHH6Nnz57w8vKqMqhCdTmke/fukMlkMDY2hqenp9qrogN1hw4dqnS4Pnv2bL3LVuGll16CsbFxg/tnGBsbIyAgAGvWrMGVK1dw9+5dHDt2rNpl//jjD/Tt27fOkWVcXV3xzjvvYP/+/Xj//fexefPmapfr0KEDTp8+rTbt9OnTaNeuHXe1oqlyMyENVdMxhSZ1794d8fHxVfKHp6cnhELV4atIJHqqfaRLly5QKpU4ceJEU4dt8OiKhZ7z9PSEXC7HN998g+effx6nT5/Gpk2b1JZxd3dHQUEBIiIi4OPjAzMzszqvZFRn5syZWL9+Pd59913Mnj0b8fHxWLp0KYKDgyEUChEdHY2IiAgMGzYMjo6OiI6ORmZmJjp06ICkpCR8//33eOGFF+Di4oL4+Hjcvn0bEydObFAMc+fOxerVq+Hl5QVvb2+sW7eu2gdSlZaWQiaTAVDdCvW///0PBQUFNIwkaXJubm6QyWRIT0/HqFGjIBAIcP36dbz44ov1utXPy8sLP/30E3r27Im8vDx8+OGHDTrbVtd+aWJiggULFmD+/PkQi8Xo168fMjMzcf36dUydOhVeXl5ISUnBnj170KtXL/z1119VBkRwd3dHUlISYmNj0apVK1haWiIgIAD+/v4YPXo01qxZw53U+OuvvzBmzBj07NkTc+fOxeTJk9GzZ0/069cPO3fuxPXr19GmTZsG1bFAIMCcOXOwbNkyTJ8+nctf5eXl3H5eeVknJyf8+eefuHPnDvr37w9bW1scPnwYSqUS7du3r/Y7ahsNqsK8efMwfPhwtGvXDo8ePcLx48fRoUOHapd9//33/9/e/YMk88dxAP88kZ6CiWJCBnonWUODNmSDBY0uDUGRQZTQ1FD0B6olbKgGCQqqqaGiJSJpaGoJwiG3IgiXGqwpGqMosHg/gySPdc9ToM+vnn7vF9x03Pf7vcMv3oe79/ckGAzKzMyMRCIRSaVSsrKyUlAcaZomyWRSuru7RVGUghWtiL67WCwmbW1t4vF4pLOzU8rKyuT09FTOzs7ybxZomiYHBwfS3NwsiqK8+zrWC03TJBqNSn9/vywtLUkgEJDLy0u5ubmRrq6uv3la398nZzyoBH4NWulZWFiAy+WC2WxGOBzG5uZmQYgQAAYGBuBwOCAimJ6eBqAfHAwEAvn9egHqw8NDBINBGI1GVFVVYXJyEtlsFgCQTqcRDofhdDqhKArq6uqwvLwMALi+vkZ7eztcLheMRiNUVUUsFsPz87PuOf0uvJ3NZjE8PAyr1QqbzYaxsTH09fW9CW+LSH6rqKhAMBhEIpF40w/D21QKW1tbaGlpAQAkk0n4fL4PH3t8fIzGxkaYTCbU1tZiZ2fnzdyUP4S3gT/PSyAXCJ+dnYWqqjAYDPB4PAWhyfHxcTgcDlgsFkQiESwuLhbMi8fHR3R0dMBms0FEsL6+DgC4vb3F0NAQqqurYTAY4Ha70dPTg6urq/yxc3NzqKyshMViQTQaxcTExIfC26/n/v39Pex2O+LxOIBcgPPXef6yKYoCIBcub21thd1uh9lsht/vx/b2tm6fd3d3MJlMOD8//+24AGBwcBA1NTVQFAVOpxO9vb35QOvr8DYAJBIJ1NfX56/5/Px8QXupVAp+vx+KooB/1/Rfee+eQu+3/B55FbLWm8d67e7v7yMUCsFsNsNqtaKpqQmrq6v5/Xt7e/D5fCgvL4eqqgD0F5MACsPbAPDw8IDR0dH8fYfP58Pa2tqHz4n0/QB0Fvkn+uIymYx4vV45OTn5q1/83djYkJGREd2nHkT0/7C7uytTU1OSTqc/eyhERF8aX4Wif1ooFJKGhgY5OjoqedsvIdv3QmJE9L1ZLBaJx+OfPQwioi+PTyzon/T09CSZTEZEcl8IdbvdJe/j4uJCRHKrWXm93pK3T0RERPSdsLAgIiIiIqKicblZIiIiIiIqGgsLIiIiIiIqGgsLIiIiIiIqGgsLIiIiIiIqGgsLIiIiIiIqGgsLIiIiIiIqGgsLIiIiIiIqGgsLIiIiIiIq2k8gQ9xRB1znDgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x650 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_cdf(values):\n",
    "    \"\"\"\n",
    "    Computes the Cumulative Distribution Function (CDF) of the input\n",
    "    \"\"\"\n",
    "    values = np.array(values).flatten()\n",
    "    n = len(values)\n",
    "    sorted_val = np.sort(values)\n",
    "    cumulative_prob = np.arange(1, n+1) / n\n",
    "    return sorted_val, cumulative_prob\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(3, 3, figsize=(8, 6.5))\n",
    "fig.suptitle('Per-user performance metrics', y=.99)\n",
    "\n",
    "for ii in range(3):\n",
    "    for jj in range(3):\n",
    "        ax = axs[ii, jj]\n",
    "        metric = metrics[3*ii+jj]\n",
    "        ax.plot(*get_cdf(results_avg[metric]))\n",
    "        if metric == 'TBLER':\n",
    "            # Visualize BLER target\n",
    "            ax.plot([bler_target]*2, [0, 1], '--k', label='target')\n",
    "            ax.legend()\n",
    "        if metric == 'TX power [dBm]':\n",
    "            # Avoid plotting artifacts\n",
    "            ax.set_xlim(ax.get_xlim()[0]-.5, ax.get_xlim()[1]+.5)\n",
    "        ax.set_xlabel(metric)\n",
    "        ax.grid()\n",
    "        ax.set_ylabel('CDF')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that:\n",
    "\n",
    "- For simulations in the downlink (`direction = downlink`), the transmit power per user remains constant and equals the\n",
    "  base station's maximum transmit power.  \n",
    "  This occurs because in the `SionnaSYS`\n",
    "  block we chose, for simplicity, to feed the scheduler with a uniform\n",
    "  achievable rate across subcarriers, resulting in only one user being scheduled\n",
    "  per slot; \n",
    "- For uplink (`direction = uplink`), power varies significantly across users to compensate for the pathloss\n",
    "  through the open-loop procedure. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCS, SINR, and throughput\n",
    "\n",
    "We next investigate the pairwise relationship among Modulation and Coding\n",
    "Scheme (MCS), effective SINR, and throughput."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:10:37.395426Z",
     "iopub.status.busy": "2025-03-14T11:10:37.395198Z",
     "iopub.status.idle": "2025-03-14T11:10:37.982665Z",
     "shell.execute_reply": "2025-03-14T11:10:37.982055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pairplot(dict, keys, suptitle=None, figsize=2.5):\n",
    "    fig, axs = plt.subplots(len(keys), len(keys),\n",
    "                            figsize=[len(keys)*figsize]*2)\n",
    "    for row, key_row in enumerate(keys):\n",
    "        for col, key_col in enumerate(keys):\n",
    "            ax = axs[row, col]\n",
    "            ax.grid()\n",
    "            if row == col:\n",
    "                ax.hist(dict[key_row], bins=30,\n",
    "                        color='skyblue', edgecolor='k',\n",
    "                        linewidth=.5)\n",
    "            elif col > row:\n",
    "                fig.delaxes(ax)\n",
    "            else:\n",
    "                ax.scatter(dict[key_col], dict[key_row],\n",
    "                           s=16, color='skyblue', alpha=0.9,\n",
    "                           linewidths=.5, edgecolor='k')\n",
    "            ax.set_ylabel(key_row)\n",
    "            ax.set_xlabel(key_col)\n",
    "    if suptitle is not None:\n",
    "        fig.suptitle(suptitle, y=1, fontsize=17)\n",
    "    fig.tight_layout()\n",
    "    return fig, axs\n",
    "\n",
    "\n",
    "fig, axs = pairplot(results_avg,\n",
    "                    ['Effective SINR [dB]', 'MCS', '# decoded bits / slot'],\n",
    "                    suptitle='MCS, SINR, and throughput')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, as the SINR increases, higher MCS indices are used, resulting in higher throughput."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link adaptation\n",
    "\n",
    "To main goal of link adaptation (LA) is to adapt the modulation and coding scheme\n",
    "(MCS), thereby the transmission speed and reliability, to the evolving channel\n",
    "conditions.\n",
    "\n",
    "LA addresses this problem by maintaining the transport block error\n",
    "rate (TBLER) around a pre-defined target, e.g., 10%, whenever possible.  \n",
    "\n",
    "Since LA must deal with potentially outdated and noisy channel condition\n",
    "feedback, it corrects the estimated SINR by applying an offset through a\n",
    "feedback loop, known as outer-loop LA (OLLA).  \n",
    "For more details on OLLA, please see [this notebook](https://nvlabs.github.io/sionna/sys/tutorials/LinkAdaptation.html).\n",
    "\n",
    "Below we visualize the relationship among the TBLER, MCS index, and OLLA offset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:10:37.985967Z",
     "iopub.status.busy": "2025-03-14T11:10:37.985660Z",
     "iopub.status.idle": "2025-03-14T11:10:38.783234Z",
     "shell.execute_reply": "2025-03-14T11:10:38.782484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = pairplot(results_avg,\n",
    "                    ['TBLER', 'MCS', 'OLLA offset'],\n",
    "                    suptitle='TBLER, MCS, and OLLA offset')\n",
    "for ii in range(3):\n",
    "    axs[ii, 0].plot([bler_target]*2, axs[ii, 0].get_ylim(), '--k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the MCS vs. TBLER figure we observe that the TBLER of users with low MCS\n",
    "index, i.e., in bad channel conditions, is far from the target. This occurs\n",
    "since at low MCS values, further spectral efficiency reduction is not feasible,\n",
    "making it impossible to maintain the BLER at the target. For such users, the\n",
    "OLLA offset tries to over-compensate, unsuccessfully, the estimated SINR (see offset vs. MCS figure).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scheduler\n",
    "\n",
    "The main principles of proportional fairness (PF) scheduling are to \n",
    "- Distribute the available time/frequency resources uniformly across users;\n",
    "- Schedule users when their channel quality is high, relatively to their past average.\n",
    "\n",
    "The scheduler assigns a resource to the user with the highest PF metric, which\n",
    "is defined as the ratio between the achievable rate and the achieved throughput.  \n",
    "An effective PF scheduler ensures that this metric remains low across all users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-14T11:10:38.785992Z",
     "iopub.status.busy": "2025-03-14T11:10:38.785794Z",
     "iopub.status.idle": "2025-03-14T11:10:39.377689Z",
     "shell.execute_reply": "2025-03-14T11:10:39.377043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = pairplot(results_avg,\n",
    "                    ['# allocated REs / slot', 'PF metric', 'MCS'],\n",
    "                    suptitle='PF metric, allocated resources, and MCS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "Sionna SYS enables large-scale system-level simulations using\n",
    "3GPP-compliant channel models generated via Sionna PHY.\n",
    "\n",
    "We demonstrated how to integrate key SYS modules, including multicell\n",
    "topology generation, phyisical layer abstraction, and layer-2 functionalities\n",
    "such as link adaptation, scheduling, and power control.\n",
    "\n",
    "To simplify the implementation, we assume *perfect* channel estimation and *exclude* handover management.\n",
    "\n",
    "For a more straightforward example of assembling the SYS modules together with\n",
    "ray-traced channels, we refer to the\n",
    "[Sionna SYS meets RT notebook](https://nvlabs.github.io/sionna/sys/tutorials/SYS_Meets_RT.html)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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